QoS-BASED COMMUNICATION PROTOCOLS IN WIRELESS SENSOR NETWORKS in .NET framework

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QoS-BASED COMMUNICATION PROTOCOLS IN WIRELESS SENSOR NETWORKS
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If the sink is mobile, the path leading to the sink and the distance between the sink and the access relay change. Hence, the sink noti es its access relay (AR) of its latest nearest-neighbor node in order to continue to communicate with the AR. If the sink-to-AR distance is increased too much and the sink-AR delay constraint is violated, then the sink needs to select another access relay. DEED is an ef cient algorithm to minimize energy consumption and meet delay requirements. However, it does not propose service differentiation among ows toward different sinks. Furthermore, decrease of reliability in data delivery due to possible link errors in wireless channels is not addressed. Additional mechanisms to address path changes due to link errors and congestion are needed. DEED adjusts the delay/distance parameter to handle congestion, but this does not guarantee avoiding highly congested local spots. Furthermore, these additional mechanisms must be local and should not alter the overall tree structure. 13.4.9 QoS for Data Relaying in Hierarchical WSNs [27] This study presented in reference [27] addresses the selection of one or more routes from sensors to a base station. The routes are chosen such as to satisfy the delay requirements. In this study, large heterogeneous WSNs are considered which are organized in three tiers of hierarchy: a base station (BS), relay nodes (RN), and nally sensor nodes acting as data sources. Relay nodes are placed such that connectivity is maintained. Additional relay nodes are placed to improve energy consumption and reduce interference. However, increasing the number of relay nodes increases the end-to-end delay. Furthermore, relay nodes that are closer to the base station consume more energy compared to others. To address these issues, a hybrid approach is proposed that introduces relay gateways (RG) that receive data from relay nodes and send them directly (in a single hop) to the base station. RGs are pre-deployed in the network and are stationary. The routing decisions at RN-RG and RN-RN communication level consider the system lifetime as a constraint. System lifetime is de ned as the time until at least one RN or RG depletes its energy supply. The lifetime of a node is modeled by the maximum amount of traf c it can handle, which is called the node capacity. The routing from sensors to relay nodes is assumed to be handled by low level protocols. Hence, this protocol only deals with ef cient routing of data among relay nodes by delivering it to one of the RGs while meeting the delay requirements. Two different cases of selecting a relay path are proposed: multipath relaying with delay constraints (MPD) and unconstrained multipath relaying (MP). Two different algorithms that are both centralized and optimal are proposed to solve these problems. In the MP problem, the aim is to minimize the end-to-end communication cost while meeting the capacity constraints of RNs and RGs. However, this problem does not deal with end-to-end delays. The WSN is modeled as a directed graph composed of a set of vertices (composed of RNs and RGs) and a set of arcs which represent the edges between vertices. The cost c of sending a packet over an arc is de ned as a function of individual arcs. Furthermore, the ow of data over an arc is de ned as a separate function x, which is used to model the routing decisions of every RN.
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QoS-BASED COMPUTATION WITH COMMUNICATION SUPPORT
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Hence the total cost of communication over the selected arcs from source RN to RG, x(a)c(a), is minimized, where a is an arc. The capacity of a node i is represented as (i), whereas the total amount of data forwarded to a relay from sensors is (i), which is called the demand of i. The set of all arcs entering a node i is (i ), and the set of all arcs leaving i is (i+). The minimization of total cost is subject to x( (i+)) x( (i )) = (i), meaning that the demand of a relay node i is equal to the net ow into i, that is, x( (i )) t (i) (i), where it is assumed that transmission requires t/(1 t) more energy than reception. The MP problem is modeled as a transshipment problem, which is a classical problem in operations research. The transshipment problem can be solved in strongly polynomial time. In the MPD problem, apart from minimizing the total cost and meeting capacity constraints, the total number of intermediary nodes on a path should not be larger than a given value to limit the total delay. Hence, packets that originate at different RNs should be distinguished. The delay constraint can be formulated either using ow functions or using feasible paths. In the former, relay nodes increment a hop-count index that is assigned to individual ows. In the latter, the set of all feasible directed paths P(r, k) originated from an RN r and ending at an RG k is de ned. The paths with lengths smaller than a threshold are chosen. However, the demand of every node should be met and the capacity of all nodes should be observed. The MPD with feasible paths formulation can be solved optimally using a linear program. The advantage of feasible path formulation over the ow function formulation is that the number of constraints is linear in the size of the graph. Column generation, which is an implementation of the simplex algorithm for solving linear programs, is used to nd a solution to the MPD problem. This work is based on a graph representation of a WSN, which is used to nd QoS-based paths in an hierarchical structure. However, methods to handle network dynamics is not considered. Furthermore, since the algorithm is centralized, it is assumed that every parameter, including the topology, link bandwidths, and link costs, are known at a central location, which is not very practical. A distributed implementation is needed to ensure its practical applicability.
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13.5 QoS-BASED COMPUTATION WITH COMMUNICATION SUPPORT The idea of reducing the communicated data volume through methods like data aggregation has been recognized as a means to reduce energy consumption and prolong WSN lifetime. While majority of in-network processing proposals involve simple operations, more complex algorithms have recently been proposed to process higher data volumes such as in video sensor networks. The main idea behind such proposals is to leverage the collective processing power of individual sensor nodes to run complex processing applications. To fully utilize the collective processing power of sensor nodes, solutions from parallel processing literature have been adopted. The proposed methods also have strong connections with the communication protocols: The exchange of intermediate results occurs over shared wireless channels, which is
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