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Science, Technology and Medicine open access publisher.Publish, read and share novel research. Wireless Channel Model with Markov Chains Using MATLABDiana Alejandra Sanchez-Salas1, Jose Luis Cuevas-Ruiz1 and Miguel Gonzalez-Mendoza1[1] Tecnologico de Monterrey, Campus Estado de Mexico, Mexico1. Nigeria’s nominal Gross Domestic Product (GDP) could grow by 3% in 2017 if the country manages to achieve a 5-fold increase in its broadband infrastructure by the same time.
This claim was made by the Nigeria’s Minister of Communication Technology to highlight the significance of Nigeria’s National Broadband Strategy and Roadmap (NBSR), which seeks to significantly improve internet access across Nigeria in the five year period between 2013 and 2018.
The NBSR sets out several broad strategies that should result in policies favorable towards the improvement of information and communication technology (ICT) networks, reduce rollout costs, increasing 3G wireless network to at least 80% of the population, open more bandwidth for broadband services, especially 4G (Long Term Evolution) networks. Nigeria is the largest telecoms industry in Africa and the Middle East, and there have been several government projects to ensure that as many of the targets are met. Nigeria’s GDP is currently estimated at 8%, and with an unserved population of about 50m, the potential for a surge in GDP is not far off; as a matter of fact, the minister’s claim is backed by the findings of a major player in global telecoms. A 2011 study by Ericsson and Chalmers University of Technology found that doubling broadband speeds correlated with an average GDP growth of 0.3% in 33 developed nations, including the USA, Italy, New Zealand, and Norway. The potential for growth in a developing country such as Nigeria is far greater than that of developed countries, so if all impediments are diligently addressed, this goal may just be within reach. The FCC recently announced plans to open up previously restricted frequency spectra to general Wi-Fi use, although it was originally set aside for vehicle-to-vehicle (V2V) communications, along with use by government entities such as the FAA. The FCC is acting in part because of pressure from the Obama administration to open up more of the air waves for data-hungry devices.
But the frequencies the FCC wants to open to new Wi-Fi applications overlap with the 5.9 GHz band set aside for future V2V communication. ITS America recently sent a letter to the FCC signed by automakers and others, including the AAA and state DOT officials, warning the FCC that the new Wi-Fi networks could interfere with wireless communication between connected cars. ITS America has another federal agency on its side: the National Highway Transportation Safety Administration. But there are ways to share spectrum, and enough time to figure out how to do it, according Egil Juliussen, an analyst with IHS Automotive.
At its meeting on February 20 the agency decided to move ahead with its spectrum sharing plan for the 5 GHz band. IntroductionSupply chain is a set of activities involving a group of commercial actors to create a product or a service to satisfy a customer demand. Enter your email address to receive exclusive news, event info, and contest information from Stream Africa.
The Intelligent Transportation Society of America (ITS America) is concerned that the federal agency is setting up a collision between competing interests. In 2010 President Obama signed a memorandum designed to increase the sharing of airwaves to alleviate a shortage of frequencies, due in part to the public’s increased appetite for Wi-Fi. NHTSA along with ITS America, automakers and others are currently conducting a connected car field trial in and around Ann Arbor, Michigan that involves 3,000 vehicles. He acknowledges that tests need to be conducted to determine that the frequencies are not close enough to cause interference. But it too will take awhile since the FCC will be collecting comments before proceeding with rule making, and it could take at least a year or more for NTIA to sort out potential interference issues. The letter F means inflammable, F+ means very inflammable, T means toxic, Xi means very irritant, means O oxidizing, Xn means noxious, N means polluting and C means corrosive.4. The actors are the ones who form the supply chain, they are suppliers, transporters, manufacturers, distributors, retailers and customers.
It’s a large piece of a puzzle that could nearly eliminate car crashes, but an initiative from the FCC could put it in jeopardy.
FCC Chairman Julius Genachowski announced at the Consumer Electronics Show in January his agency’s plan to clear 195 MHz of spectrum in the 5 GHz band for Wi-Fi use. The star-crossed company had ambitious plans to build a large-scale wireless broadband network that was initially approved by the FCC.
The goal of the test is to see how vehicles communicate with each other and traffic infrastructure to share information such as speed and location to reduce collisions by warning drivers of road hazards and dangerous highway conditions.
And while opening up the spectrum could eventually mean faster Wi-Fi, it may also mean you’ll need a new router in addition to being a speed bump on the road to the connected car. So, add us to your ad blocker’s whitelist or pay $1 per week for an ad-free version of WIRED. The channel model helps to evaluate the performance of the system and to compare different techniques to mitigate the perturbations so the best fitted solution can be implemented according to the presented problem. In this chapter, our objective is to describe dangers for people, environment and goods from dangerous goods exploitation. But later the agency ruled that LightSquared’s plans would interfere with GPS transmissions, and that the airwaves weren’t big enough for both technologies. Based on the results of the field trial, NHTSA will decide by the end of this year whether to mandate the V2V technology in new cars and implement it in the years to come.
Julissen also says that he doesn’t expect connected cars to hit the road until until around 2019. The simplest model is the free space loss which considers no obstacles between transmitter and receptor.
To understand the potential of chaining event we elaborate accident scenario to describe the consequences of such accidents.This risk study in warehouse provides insights to elaborate a risks management strategy relying on communicating objects such as RFID tags and wireless sensors. Another option to describe a channel is by using statistical models which are based on probability density functions (pdf). Then, by exploiting the technical features that provides these smart items, it becomes possible to detect in real time any accident risks and to react in consequence.This chapter is organized as follows. Although they do not describe the behavior of the sent signal in a complete way, they give a good approximation of the conditions of the channel in a certain moment. Part 3 describes dangerous goods in logistics and notably the risks inherent to their storages. Current regulations such as CLP is presented, it provides a standard in the European Union to classify and identify dangerous goods. Risks relative to dangerous goods manipulation are presented, segregation strategies and storage constraints must be respected to maintain the security within a warehouse.
States of Markov chains could be defined in agreement to channel conditions of an scenario, like it was done by E. In part 4, a risk study is lead to extract three scenarios that describe the domino effect consecutive to accidents. Lutz who defined a two state channel model, one classified as good and the other one classified as bad depending on the conditions and therefore the attenuation level. These scenario are treated further in part 5 when communicating objects are integrated at pallet level.
Vucetic which characterizes the wireless channel of a certain area in Australia with a four state Markov chain by combining four different types of statistical channels.Next sections explain in detail how to create a wireless channel model using Markov chains and its application in a communication system with a coder and modulator.
Supply chain management in logisticsDepending on actors involved in Supply Chain (SC) (suppliers, transporters, customers), SC definitions can differ regarding the described interactions and dependences between actors.
A SC is a succession of activities related to a specific function performed by one or several actors in order to satisfy customer demands. An analysis with a semimarkovian channel is explained in the last section and a comparative analysis is done between this model and that from Markov chains.
Supply chain managementA SC exists if at least two companies work together to the production of a particular product or service.
If this association is explicitly guided to improve the performance then we describe this as supply chain management, also called SCM.From a general point of view, the SCM can be defined as the coordination between companies internal and external activities. Markov chainsOne type of stochastic process is Markov chains, named after Andrei Markov who studied the transitions between consonants and vowels in a poem at the beginning of the 20th century. The goal of this management is to improve the SC performance on long term basis so that each actor of the SC can take benefit of this global management.The SCM consists of managing the whole organizations involved in the delivery of a final product or service. Its aim is, on one hand, to produce products relying on information received from the customer needs and, on the other hand, to minimize the different supply, production, warehousing and delivery costs.
X(t) = i represents that the process is in state i at an instant of time t and take discrete values.
The SCM gathers two parts: the integration of the company along the SC and the coordination between the physical, information and financial flows.
It is said that there is a fixed probability pij that chain goes from a state i to a state j in the next time t.
The main objective of the SCM is to improve the competitiveness of companies by minimizing the costs while the quality of service required by the customer is guaranteed.3.
Dangerous goods management in logisticsA good is considered as dangerous when it may present a danger on population, environment or on infrastructure according to its physicochemical properties or because of the reactions it can imply.Activities involving dangerous goods concern all parts of the world. A global regulation is needed and should be coordinated with local authorities to make laws more reliable and respected. This statement is illustrated in Figure 1 which shows that the system can change from one condition to another or continues in the same state.

Since the logistics tend to be global, the control should be also global for a better efficiency.Laws related to the use, the loading, the unloading, the storage, the transportation and the handling of dangerous material differ according to activities, status or modality of transports. Countries rely on international recommendations proposed by the Organization of United Nations to regulate the operation on dangerous goods.The UN proposes recommendations for the dangerous goods since 1957 and updates periodically its texts.
It is a reference which provides the main recommendations related to the dangerous goods notably about the different methods of transport: air, road, railway, canals and sea. Probability transition matrixThe switch between states is established in the probability transition matrix P. Dangerous goods identificationConsidering the important number of substances, there is a clear need for dangerous goods classification. Obligations under CLPCLP provides a global obligation for all suppliers in the supply chain to cooperate. Transition diagramA probability transition matrix can be also represented as a diagram called transition diagram. TerminologyA new terminology is used, terms of existing regulation are kept whereas news are adopted. Each node 0 represents a state of the Markov chain indicated with a number inside; an arrow ? connects state i with state j if a transition exists and the transition probability pij is written on that connecting arrow, even if the transition is to the same state. According to these three criterion, the definition of hazard classes helps to classify a substance.
Steady-state vectorAnother important element in Markov chains is the steady-state vector ?, which represents the total appearing percentage of a state in a Markov chain. Classification of substancesCLP possesses specific criteria of classifications that are rules that allow associating a substance to a class of hazard or a category in this class. The health hazards are relative to acute toxicity, skin corrosion, irritation and sensitization.
Creation of a Markov chains in MatlabIn this section you will find the steps and the code to create a Markov chain using Matlab for two and three states. Risks in dangerous goods storageIn the context of logistics the main considered risks are relative to the physical or information flows disruption. The first step for the creation of a chain is to establish a transition matrix that indicates the switch between states.
Risk definitionThe Process Safety Management (PSM) defines risk as follows:"Risk is defined as a measure of frequency and severity of harm due to a hazard. Since this is a stochastic event, the first sample and the switch between stated will be obtained in a random way but always following the characteristics of P. The precise steps to create the Matlab code are listed below.Generate a random integer number between 1 and 2 (because of the two state markovian chain).
To this end, a first step is needed to identify substances as a function of their potential chemical reactions.In order to prevent any storage of incompatible chemicals and risk of chemical reaction in case of wrong handling, seggregation strategies are used.
The input is the 3 x 3 size probability transition matrix, like the one shown in (13) and the output vector will be also the vector containing the Markov chain with the characteristics set by P.
MethodologyThe methodology used for scenarios definition aims at characterizing, analyzing, and assessing the risks in warehouse.
The steps are listed next.Generate a random integer number between 1 and 3 (because now it is a three-state model). To verify that the chain follows also the steady-state vector it is necessary to count the number of times each state appeared in the chain and divide each count by the total number of samples (size of the output vector).Two and three-state Markov chains Matlab programs described above represent the wireless transmission channel that will be implemented in a communication system. Risk evaluationAn analysis of the warehouse neighborhood is conducted in order to identify targets and their vulnerabilities and to characterize the severity of the dangerous phenomena.
Implementation of a Markov chains in MatlabAnother alternative to characterize a wireless channel is using Markov chains as it was said before. This model is a better option than statistic channels (AWGN, Rice, Rayleigh) because the former represent a statistical channel combination of the latter.
Scenario 1: Aerosol explosionThe warehouse is likely to receive products classified under the heading 1412 UNcode, bottles generating aerosols.
The propellant gases contained in these bottles is most of the time of butane or propane under pressure.In this scenario, we describe the explosion of aerosol (air freshener). Markov chains will be used as a base to create a wireless channel model and will represent the multiplicative fading, which is right after the transmitter, and will be switching between the possible channels according to the probability transition matrix, as it is shown in Figure 4. From now, this model will be called as a “markovian channel”.To have a better understanding of this markovian model, several examples with different probability transition with its steady-state vector, calculated using (9), are proposed.
The codes for the creation of a two and three state Markov chains are used to produce results.
Common bottles have a varying volume from 50 to 500 ml and contain between 30 and 150 G of product plus propellant gas.The behavior in fire of aerosols generators of depends on one hand nature on propellant gas and on the other hand on the nature of the conditioned liquid. Interpretations are based on BER curves obtained by implementing the communication system and the markovian channel of figures below. Therefore, if the propellant gas is a flammable gas standard butane or propane, there is a risk of explosion.
The bottles are then dispersed by missile effect and can be in their turn a propagator of fire.In the same way, if the liquids contained are flammable liquids, they will support the fire. Two-state Markov channel modelAs it was said before, this is the simplest model in Markov chains. Preliminary risk analysisBased on the calculating probability scale and the calculation of gravity scale, this scenario occurred and may occur throughout the life of the installations and no person will be impacted.The method used to assess the effects of an explosion of aerosols in a warehouse is multi-energy model. Explosion cloud formed by the gas contained in a bottle would not reach the thresholds of overpressure. Regardless of the distance to the cloud, a target suffers less than 20 mbar pressure which causes no human and material damage.
Conversely, channel P2chan2 establish that channel 2 (Rayleigh) will appear all the time, this channels can be seen as an urban environment where there are a lot of obstacles between transmitter and receiver that will cause the multipath or also it can represents an user who is moving from one point to another one. The ammo-nitrates high or medium dosage, French standards or European are difficult to detonate in the absence of contamination. It can be appreciated in Figure 5 that the resulting BER curve for P2chan1 is exactly the same as the one resulting from the simulation of the Rice K = 14 dB statistical channel. Likewise, BER curve generated from P2chan2 is exactly the same to the simulation where only Rayleigh statistical channel is implemented.To proof the Markov channel follows the characteristics of (16) and (17), a counting of the transitions and the number of times both channels appeared is realized from the created Markov chain. The size of the simulated sent message was 100,000 bits, so the results of the count are given as percentages, which is equivalent to the probabilities of the transition matrixes. The fire or explosion of aerosols and fertilizer is due to inflammation of a palette (mechanical heating, inflammation, engine, non-compliance with the prohibition of smoking). This means that 100% of chances there was a transition from channel 1 to 1 and from channel 2 to 1.
We left the assumption that a cigarette would be has the origin of the starting point of fire. We leave the principles which another vehicle charged with manure would be been parked on the quay dimensioned. For channel P2chan4 probabilities are opposite than the other channel, in other words, there is a greater probability of having LOS than multipath, like in a rural area.
Scenario 3: Hazardous liquid spillAccidental discharge is related to the presence of liquid products on site. Resulting BER curves for both channels are shown in Figure 8, where it shows that BER curves from markovian channels of (20) and (22) are very close to that curves resulting from simulating only the correspondent statistical channel but they are not the same, proving the combination of the channels according to the probability transition matrixes. A leakage of hazardous substances can occur due to the fall of one or more pallets of a rack but also due to a weakness in on (or a series) of container (bottle, can). A probability transition matrix shown in (24) with its steady-state vector in (25) are presented as a new wireless channel model. In this channel model the user has LOS the most part of the time but in some instants experiments multipath. Risk evaluationBecause of the loss of integrity of a container, a liquid will flow and form a pool of dangerous liquid which mainly function of the topography, the viscosity of the product and quantity.The multitude of references and stored products do not enable to study exhaustively all dangerous chemical reactions. This seems a semi urban environment because it can exists some building that cause multipath but they are not that tall to block the line of sight. Preliminary risk analysisThis scenario occurred and may occur throughout the life of the installations and no person will be impacted. Scenarios modellingIn this section, we model the scenarios presented previously by UML diagrams to describe the sequential chain of events following an accident.
In particular, we are interested in identifying the main damages and consequences of accidents involving dangerous goods.As we said, the accidents are evaluated by several measures, by the place and by the number of persons within this place.
The type of accidents we consider and which can occur during the manipulation of dangerous goods is correlated to the chemical features of those goods. Scenario 1: Aerosol explosionBased on the scenario introduced as Scenario 1, we describe how a bomb attack can occur in a warehouse. Nevertheless, by considering that terrorism attacks have a probability to miss their targets, we can assume that a remote bomb attack can potentially become a road accident when the bomb explodes at an untimely moment.We describe by an activity diagram, see Fig. The first events are relative to the way that the bomb is susceptible to explode when the trigger is activated; it can burn first before exploding.

Once the explosion happens, the shock wave provokes damages on near products and a domino effect starts from the pallet to the rack beams. The fire and explosions provoke pressure effects and thermal effects which have consequences on persons present in the stock. The damages caused by this bomb attack are measurable by three dimensions: the human, the structure and the merchandises damages.
Here the humans damages are relative to the persons working in the warehouse and who have: inhaled gases, been victims of missile effects or building collapses, been burnt. The structure damages regroup the building destructions notably the racks, the traverses and the building structure. This scenario describes the chain reaction explosion generated from the expansion of inflammable vapors produced by gas substances (previously kept under pressure in a liquid state). The effects that derive from this type of explosion are effects of excess pressure and fire balls projections and provoke damages on people, structures and goods.
Scenario 2: Fire truck incidentThis scenario involve a set of actors during the loading or the unloading of a truck. Then, the transporter is present and follows the operations while the driver handles pallets between the dock and the stock. The shock provokes the release of gas which concentrates to form a cloud around the dock and the truck. The damages caused by this accident spread on four dimensions: the human, the structure, the merchandises and the environment damages. Among them, we can cite the transporter, the driver, the person who works near the dock and near the stock. The structure damages implies the dock itself which can be deformed by the explosion, the truck which can burn and also the warehouse building which can suffer from the explosion. The merchandises destroyed are the ones present on the dock or the ones which burnt during the fire. This type of accident provokes the formation of toxic gas clouds which are polluting the environment.
Before and after the cloud explodes the mixed gases stay in suspension and moves with the ambient air. Scenario 3: Hazardous liquid spiltThe third scenario describes how a rack failure is susceptible to involve a collapse of pallets and then provoke an accident. The pallets fall over each others contributing to gas emanations and liquid spreads, see Fig. Here, we don’t use any aggravating facts and we consider that the gas emanation and liquid spreads stay stable and don’t provoke yet any explosions. Integration of communicating objects for a dynamic risk managementOver the past few years, communicating objects have become a new emergent solution to secure the international trade by providing tracking tools [2–4] and environmental monitoring [5, 6].
Then, tracking tools provide geolocation solutions that can be used to detect any risks inherent to goods location. The studied supply chain manipulates chemical substances that represent potentially hazard for persons and environment. Supply chain actors and activitiesThe studied supply chain involve different actors that manipulate chemical substances:Chemicals providers are located in Asia.
Operators unload containers to store pallets in racks, load pallets in containers and finally send them to the downstream supply chain actor.Customers, they are big-box stores around Paris. They receive containers, unload them and transfer chemicals from communicating pallets into common ones and send back them to the pallet-provider.The communicating objects are integrated at the pallet level. The cost is shared by the different actors and the programming of communicating objects is realized by an upstream actor: the pallet-provider.
RFID technologiesA basic RFID system contains three elements; a tag, a reader and a middleware.
An RFID tag is read when it receives radio signals from the reader and sends data back to the reader. Wireless Sensor NetworksWireless Sensor Networks (WSANs) refer to a group of sensors linked by a wireless medium to carry out phenomenon sensing and acting in consequence. Risk assessment with communicating objectsThe proposed risk assessment is based on the chemicals physicochemical properties.
Depending on these properties, the substances are classified to a specific class of hazard according to the CLP regulation. From this classification, constraints are deduced and must be respected all along the supply chain.
The combination of the identified constraints and the environment of chemicals are the source of risks, see Fig. The risk assessment is based on classification and activities that define constraints to be respected along the supply chain.Different sensors are associated to form a cluster of sensors able to monitor a set of constraints. Sensors send data to a centralized software that translates them into accident risks in case of unusual values.
Scenarios: Explosion and chemical reactions on stowIn the first scenario, the events occur at dock where the truck is stowed and a forklift driver is unloading pallet and put them on the dock before storing them further on rack. The dangerous goods considered are aerosols and are commercialized as air fresheners.Sensors present on pallets send periodically their data to the software.
A terrorist triggers a bomb explosion (remote activation by mobile phone for example) from a pallet that is still on the truck cargo.
Different events occur from this explosion: thermal and smoke emanation, release of toxic gases and missile effects. Pallets in the container and on the dock will be damaged by the blast wave, the main consequence on goods is the loss of aerosols containment.
Consequences are expected on environment, on warehouse workers and on warehouse building structure.As soon as the explosion occurred, sensors present on pallets that suffer from the bomb consequences will immediately be damaged and will not emit anymore whereas sensors present on dock will emit only few seconds before being destroyed by fire. The software that collects data will receive unusual values such as increasing heat and gas pressure and in consequence will sends an alert message to the warehouse manager. This last is responsible of the security maintain and will understand that an accident occurred on a dock. He will then deploy emergency procedures and will be helped by the software that provide strategic information with the alert messages. The software will send information about the pallet whose sensors are broken, the CLP designation of goods present on these pallets will be sent.
An handling error such as a pallet is poorly loaded and taken by the forklift driver with a slight offset provokes a collision between the handled pallet and others already unloaded on the dock. This collision provokes a liquid leakage on the dock and a trigger event like a spark or an electrical arc created by a cell phone provokes a fire.This accident is treated by the same way than the scenario 1. Scenarios: Pallet collapse and domino effect in stockThe scenario 3 takes place in the warehouse storage area and involves operator, pallets and racks.
During operator movements, we assume that the rack structure can be damaged by forklift collisions and the accumulations of such shocks can potentially provoke rack damage that can lead to the rack destruction due to the supported pallet weights.The detection of a collision between a forklift and a rack can be detected relying on the stability sensor values transmitted by sensors. If more than four pallet have their stability values modified between 20% and 40%, the software translates this situation into "rack stability" alert and sends it to the retailer manager. From this alert, the retailer manager will trigger emergency procedures to check the involved rack. Then, the software provides different information relative to pallets whose stabilities have evolved, these information are exploited by the retailer manager to locate them using his internal WMS (Warehouse Management Software). Then, if the pallets whose stabilities have evolved are neighbours and located near the rack structure, an internal alert of "rack collision" is emitted and the forklift drivers that operate in the cell are convoked for a further debriefing.In the scenario 4, we assume that forklift drivers move in the stock area when a forklift loses its cargo that fails. Stability values transmitted by sensors of this pallet will trigger a "pallet fail" by the software. The software provide also gps location of the involved pallet but this information cannot be exploited by the warehouse manager because none mapping exists between the geolocation and the location in the storage area.
For that reason, an internal alarm is triggered in the corresponding cell to inform forklift drivers about a pallet fallen but none other accurate information are available.6.
ConclusionIn the global context of supply chain management, the three common flows of goods, information and physical are subject to modelling regarding optimization concerns. Risk studies allow identifying logistics missions that may represent danger on persons, environment and goods. Scenarios are then extracted to describe risk management strategies and then improving emergency procedures.In this chapter, three scenarios are developed to answer specific events, located in quite precise places and representing real risks. It is the analysis of risks, its causes and its impacts which will enable to set up one of the mechanisms of detection and prevention.The integration of communicating objects such as RFID tags and wireless sensors offer new features for a dynamic risk assessment. Sensors are then used for accident risk detections, collected data are transmitted to a centralized software that is able to compare them with confidence intervals and in case of mismatches alerts are sent.

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