Energy‐efficient capacity‐constrained routing in wireless sensor networks

Abstract

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.Abstract-A critical issue in the design of routing protocols for wireless sensor networks is the efficient utilization of resources such as scarce bandwidth and limited energy supply. Many routing schemes proposed in the literature try to minimize the energy consumed in routing or maximize the lifetime of the sensor network without taking into consideration limited capacities of nodes and wireless links. This can lead to congestion, increased delay, packet losses and ultimately to retransmission of packets, which will waste considerable amount of energy. This paper presents a Minimum-cost Capacity-constrained Routing (MCCR) protocol which minimize the total energy consumed in routing while guaranteeing that the total load on each sensor node and on each wireless link does not exceed its capacity. The protocol is derived from polynomial-time minimum-cost flow algorithms. Therefore protocol is simple and scalable. The paper improves the routing protocol in [1] to incorporate integrality, node capacity and link capacity constraints. This improved protocol is called Maximum Lifetime Capacity-constrained Routing (MLCR). The objective of MLCR protocol is to maximize the time until the first battery drains its energy subject to the node capacity and link capacity constraints. A strongly polynomial time algorithm is proposed for a special case of MLCR problem when the energy consumed in transmission by a sensor node is constant. Simulations are performed to analyzed the performance of the proposed protocols.

Enhanced Route Selection for Energy Efficiency in Wireless Sensor Networks

“…The routing of the data packets to the base station on these minimumcost paths is efficient, so long as the rate of information generation is low or the channel bandwidth is sufficiently high. Still, if the nodes generate data constantly and the bandwidth is constrained, then routing data on the minimum-cost paths can overload wireless links close to the base station [6]. Therefore, a routing protocol must take into consideration the wireless channel bandwidth limitations, otherwise, it might route the packets over highly-congested links and paths.…”

“…Therefore, a routing protocol must take into consideration the wireless channel bandwidth limitations, otherwise, it might route the packets over highly-congested links and paths. This will lead to increase in congestion, increased delay and packet losses, which in turn will cause retransmission of packets, thereby, increasing energy consumption [6].…”

Review of Optimization Problems in Wireless Sensor Networks

“…It is a kind of combination of maximum flow (getting as much flow as possible from the source to the sink) and shortest path (traveling from the source to the sink with minimum cost). The problem addressed in (Xu et al, 2008) has the same structure as that found in Patel et al (2006), but the objective is utility maximization, which is a nonlinear convex function of the transmission rate. The problem is solved using the Lagrangian method.…”

“…To ensure that the solution will not generate scenarios in which the traffic load is unfair for the nodes in the network, the flow transmitted by a node has to be less than a given fraction of the total flow generated by the network. Patel et al (2006) add the following two constraints to the basic version of the routing problem: (i) the link capacity constraint where the rate (the number of packets per unit time) at each link has to be smaller than its capacity, and (ii) the node capacity constraint where the number of packets that a node can process in a unit time has to be smaller than its given capacity. The proposed algorithm is centralized and aims to find a maximum flow with the smallest possible energy cost.…”

Έλεγχος Ισχύος Και Εξοικονόμηση Ενέργειας Σε Ασύρματα Δίκτυα Αισθητήρων

“…Ως εκ τούτου, όλα τα πρωτόκολλα δρομολόγησης θα πρέπει πάντοτε να λαμβάνουν υπόψη τους περιορισμούς που θέτει το εύρος του ασύρματου καναλιού, διότι, σε διαφορετική περίπτωση, τα πακέτα δεδομένων θα δρομολογούνται σε μονοπάτια και ζεύξεις όπου παρατηρείται πάρα πολύ μεγάλη συμφόρηση. Αυτό το γεγονός θα συμβάλει στην περαιτέρω αύξηση της συμφόρησης, στην αύξηση της χρονικής καθυστέρησης, αλλά και στην αύξηση απωλειών πακέτων, με άμεσο επακόλουθο την εκ νέου αποστολή πακέτων, και κατά συνέπεια στην αύξηση της κατανάλωσης ενέργειας[172].…”