Ambitlick Solutions Communication Cost Minimization in Wireless Sensor and Actor Networks For Road SurveillanceObjective: To reduce the communication cost of data transmission in WSANs using Dijkstra’salgorithmAbstract: In recent years, wireless sensor and actor networks (WSANs) have been extensivelydeployed to monitor physical environment and facilitate decision making based on datacollected. Emerging applications such as road surveillance highlight some interesting researchissues in WSANs, including coordination problems in sensor–actor or actor–actorcommunications, the issue of choosing a set of working actors for coordinating data transmissionin a road sensor and actor network with minimum communication cost. A theoretical model isintroduced to analyze the communication cost of data transmission in WSANs, and the sensor–actor coordination problem is formulated as an optimization problem. And it can be reducedusing dynamic programming algorithm. A novel graph-based algorithm is also proposed with acommunication-cost graph used to depict the cost of data transmission and a modified Dijkstra’salgorithm to find optimal solutions in reduced time complexity.Algorithm Used : 1. dynamic programming algorithm 2. graph-based algorithm 3. Dijkstra’s algorithm
Ambitlick SolutionsSYSTEM ANALYSES :Existing System:1. Selective communication policies in WSN.Proposed System:1. Optimal Selective Forwarding schemes:• when sensors maximize the importance of their own transmitted messages;• when sensors maximize the importance of messages that have been successfullyretransmitted by at least one of its neighbors; and• when sensors maximize the importance of messages that successfully arrive to the sink2. Introducing the battary power with actual energy , says there is enough energy for areasonable number of transmissions. If the node has some battery for only a few transmissions,the forwarding threshold should start to oscillate and decreases.
Ambitlick SolutionsOver all diagram : Wireless Node Road Surveillance network Dijkstra’s Deployment algorithm Inactive Actor Working actor Virtual Actor Energy Sensors /Actors - detect events Send their data to nearby actors Inactive Active Actors Actors (long-range communications) Dynamic Programming Normal Solution Sensor Sink
Ambitlick SolutionsData Flow Diagram :Level 0 : Wireless Sensor Energy / Batt Sesor / Actor Node In RSNLevel 1: Detect Send data to Near- Dijkstra’s algorithm Actor or Sink EventLevel 2: Active long-range Dynamic sensor communications Programming Solution SINK
Ambitlick SolutionsLevel 3 : Inactive Act as normal Sensors sensorUse case diagram : Energy/Batt Send data to Near- Actor or Sink Active Actors SINK long-range communications Dynamic Programming Solution
Ambitlick SolutionsSequence Diagram :Active Actors Dijkstra’s SINK Sensor algorithm Energy/Batt Power Send data to Near- Actor or Sink Inactive sensor / Actor Dynamic Programming Solution
Ambitlick Solutions1.Securing Wireless Sensor Networks: A Survey The significant advances of hardware manufacturing technology and the developmentof efficient software algorithms make technically and economically feasible a network composedof numerous, small, low-cost sensors using wireless communications, that is, a wireless sensornetwork. WSNs have attracted intensive interest from both academia and industry due to theirwide application in civil and military scenarios. In hostile scenarios, it is very important toprotect WSNs from malicious attacks. Due to various resource limitations and the salient featuresof a wireless sensor network, the security design for such networks is significantly challenging.In this article, we present a comprehensive survey of WSN security issues that were investigatedby researchers in recent years and that shed light on future directions for WSN security.2. On maintaining sensor–actor connectivity in wireless sensor and actor networks: In wireless sensor and actor networks (WSANs), a group of sensors and actors areconnected by a wireless medium to perform distributed sensing and acting tasks. Sensors usuallygather information in an event area and pass it on to actors, which are resource-rich devices thatmake decisions and perform necessary actions. Therefore, it is vital to maintain connectionsbetween sensors and actors for effective sensor- actor coordination. In this paper, we first defineseveral sensor- actor connection requirements, including weak and strong actor-connectivity, andthen propose several local solutions that put as many sensors as possible to sleep for energysaving purposes, while meeting different actor-connectivity requirements. We also prove therelationship between the proposed actor-connectivity and the connectivity in regular graphs,
Ambitlick Solutionswhich helps with the implementation of the proposed solutions. Comprehensive performanceanalysis is conducted through simulations.3. Communication and coordination in wireless sensor and actor networks: In this paper, coordination and communication problems in wireless sensor and actornetworks (WSANs) are jointly addressed in a unifying framework. A sensor-actor coordinationmodel is proposed based on an event-driven partitioning paradigm. Sensors are partitioned intodifferent sets, and each set is constituted by a data-delivery tree associated with a different actor.The optimal solution for the partitioning strategy is determined by mathematical programming,and a distributed solution is proposed. In addition, a new model for the actor-actor coordinationproblem is introduced. The actor coordination is formulated as a task assignment optimizationproblem for a class of coordination problems in which the area to be acted upon needs to beoptimally split among different actors. An auction-based distributed solution of the problem isalso presented. Performance evaluation shows how global network objectives, such ascompliance with real-time constraints and minimum energy consumption, can be achieved in theproposed framework with simple interactions between sensors and actors that are suitable forlarge-scale networks of energy-constrained devices.4. Wireless sensor and actor networks: Research challenges With the maturing of research in wireless sensor networks (WSN) and the more recentadvances in wireless sensor and actor networks (WSAN), there has been an increasing interest inheterogeneous self-organizing networks with multiple types of nodes that possess differentcapabilities and perform diverse tasks in the networks deployment, maintenance, and applicationfunctionalities. This paper explores the conceptual and architectural challenges in the design of
Ambitlick Solutionsgeneric tools for modeling and simulation of such systems. It first addresses the modeling issues,including the diversity of node types and capabilities, the variety of possible abstractions, and theneed for vertical cross-layer integration. After a brief review of the solutions in some existingsimulation systems, the paper outlines an open architectural platform incorporating the facilitiesfor: definition of potential capabilities of network elements; formation of node types withselected capabilities and behavioral algorithms; formation of relevant environment models;configuration and initialization of the network and its environment; and scenario definition,execution and monitoringModules :Design Of RSN Network WIRELESS sensor and actor networks (WSANs), which are composed of a set of sensorsand actors linked by wireless medium to perform distributed sensing and acting tasks. Sensorsare low-cost, low-power devices with limited sensing, computation, and wireless communicationcapacities. Actors are assumed to be equipped with better processing capabilities, highertransmission power, and longer battery life. In WSANs, sensors and actors work together in data-centric applications, with sensors gathering information about the physical world and actorstaking appropriate actions on the environment A set of working actors and route sensing data between sensors and actors to minimizethe total communication cost for road surveillance.Implimentation Of Actors in sensor network
Ambitlick Solutions Sensors detect events and send their data to nearby actors. Unlike other researchassuming actors to be resource-rich nodes with unlimited power supply, we make much weakerassumptions about actors in our model: We only assume that actors are capable of sensing andperforming long-range communications. That is, in our model, actors are not necessary to bepowerful nodes; they could be resourcelimited nodes operating on batteries, or they could be justnormal sensors that are chosen to collect data and send them to the sink . Each actor has two states: working or inactive. If an actor is in the working state, it cansense events, collect data from nearby sensors, and establish long-range communication with thesink. If it is in the inactive state, it acts like a normal sensor.Network Communication Cost In network communication cost: sensor–sensor, sensor–actor, and actor–sinkcommunication. To simplify the analysis, we assume that the energy cost for unit datatransmission in each hop of sensor–sensor and sensor–actor communication is the same. Dijkstras algorithm: From the current intersection, update the distance to everyunvisited intersection that is directly connected to it. This is done by determining the sum of thedistance between an unvisited intersection and the value of the current intersection, andrelabeling the unvisited intersection with this value if it is less than its current value. After youhave updated the distances to each neighboring intersection, mark the current intersection asvisited and select the unvisited intersection with lowest distance.
Ambitlick SolutionsDynamic Programming Solution: When the system is in idle condition, the OPT algorithm turns off as many actors aspossible to save energy. For normal road surveillance, it yields the lowest energy cost, and itsperformance is insensitive to actor density. In a busy traffic environment, it keeps the workloadof working actors at a low level. The dynamic programming algorithm produce an optimal solution based on theassumption of virtual working actors. In real RSANs, working actors may be not deployed ineach intersection; thus, the solution may be not optimal in the real case. The following theoremshows that the proposed algorithms produce a near-optimal solution when there are no workingactors in the intersections.Performance Evaluvation : a. Energy Vs Node Density b. No.Of.Worling Actors Vs Node Density c. Energy Vs Actor Density d. No.Of.Worling Actors Vs Actor Density e. Communication Overhead Vs No Of Sensor Nodes
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