Power-Efficient Routing in Wireless Sensor Networks
7.4 Optimization-Based Power-Aware Routing Protocols
7.4.1 Minimum-Cost Forwarding Protocol
In [Ye] Ye et al. propose an algorithm aiming to provide message delivery through the minimum-cost path from a sensor node to the sink in a large-scale WSN. This work tries to explore a new scalable solution to the minimum-cost forwarding problem, i.e., the cost field-based approach. The cost field for packets is something similar to the gravity field for water: as water flows from high to low posts, once the cost field is established, packets flow from source to sink nodes through the minimum-cost path. In order to achieve this behavior, each message has to keep the minimum required cost from the source to the sink node as well as the consumed cost from the source to the current node. The sender always uses broadcast packets. Intermediate nodes that receive the packet forward the message only if the consumed cost of the packet plus its own minimum cost to the sink is equal to the minimum cost specified by the source node. Hence, only nodes belonging to the minimum-cost path from the source to the sink perform data forwarding. Each node has to maintain only the minimum cost from
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itself to the sink. As this approach does not need to maintain explicit path information for each packet or flow, it scales well with the number of data flows. However, before using this algorithm the cost field needs to be established.
Establishment of the cost field is started by the sink node, which broadcasts an ADV packet with a cost. Each node N that receives an ADV from a neighbor M computes the cost from itself to the sink using this message, i.e., LM+CN,M, where LMis the cost for the node M and CN,Mis the cost from M to N. If it is lower than the current LNvalue, the LNvalue is updated and a back-off timer is scheduled (or reset). When the timer expires, an ADV message containing the LNvalue is broadcast.
This protocol is efficient as long as the network is static. However, if there is a change in the topology, i.e., a node runs out of energy, the cost field has to be reestablished. Fault tolerance is low, because when a node fails many sources will not be able to forward data to the sink until the cost field has been reestablished. Although this protocol could be used with a generic cost function, the authors propose the use of energy as the path cost. So this protocol guarantees that the selected hop is always the optimum in terms of depleted energy. But while this approach achieves optimal results in terms of energy consumption, it does not optimize the network lifetime, as packets from the same node always use the same path. Load and energy consumption are thus not balanced so the most used node can quickly drain its battery.
7.4.2 Sequential Assigned Routing Protocol
The sequential assigned routing (SAR) protocol [Soh] is a table-driven, multi-path routing protocol. As compared to classical ad-hoc protocols for MANET such as the AODV or the temporally-ordered routing algorithm (TORA)∗, SAR tries to improve energy efficiency and fault tolerance in low-mobility networks such as WSNs while maintaining a desired QoS. The main idea of the SAR protocol is to route packets along multiple paths depending on the energy and QoS require-ments. In addition to energy and required QoS, the SAR protocol also takes packet priority into consideration.
In order to set up multiple paths from each node to the sink, multiple trees are built. Each tree is rooted from a one-hop neighbor of the sink. Once the trees are built, most of the nodes will belong to more than one tree, so they will have multiple disjoint paths from which the sink node can be reached.
Each path is assigned two parameters by each node: a QoS metric and an energy resource, that is, the number of packets to be sent through that path before the node energy runs out. Path selection is performed by the source node, through computation of a weighted QoS metric, obtained from the additive QoS metric multiplied by a weight coefficient depending on the packet priority. Notice that by choosing the path, the one-hop neighbor of the sink is also selected. Furthermore, having differ-ent paths to the sink, in the evdiffer-ent of failure a node can perform failure recovery, thus also improving fault tolerance. However, as the available energy of the nodes changes with time, it has to be peri-odically updated. In general this protocol is efficient, but it needs a high overhead to build the trees, maintain the tables, update the state, etc. The simulation results in [Soh] show that the SAR proto-col features better performance than algorithms that always select the minimum-energy path regard-less of network conditions. The approach used to achieve energy efficiency is to optimize a weighted metric which takes the residual energy of nodes into account. This should achieve balanced energy consumption and an improved network lifetime. To further increase energy saving, the authors
∗AODV is a source-initiated routing protocol that performs on-demand route discovery and maintenance, and relies on sequence numbers assigned by the destination to avoid loops. TORA is a link reversal algorithm that builds a directed acyclic graph (DAG) rooted at the destination in which data flows from higher to lower nodes.
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recognize the need to lower the duty cycle of the nodes by dynamically powering the radio on and off.
For this reason, in the same paper they also propose the use of self-organizing medium access control for sensor networks (SMACS), which is a MAC protocol that allows nodes to discover neighbors and create a transmission schedule to communicate in a time division multiple access (TDMA) fashion.
Each link is assigned a time slot and a random frequency band. In this way SMACS can decrease the duty cycle of the nodes as well as the likelihood of collisions.
7.4.3 Energy-Aware Routing Protocol
Theidea behind the energy-aware routing (EAR) protocol [Sha] is that always using the lowest-energy path to route data may not be the optimal choice to optimize the WSN lifetime in the long term. In fact, a routing protocol that finds an optimal path and uses only that will quickly drain the energy of the nodes on that path. This would lead to a large disparity in the energy levels of the nodes, with the risk of network partitioning. In order to enhance network survivability, that is, to maintain network connectivity as long as possible, the EAR protocol does not find a single optimal route but a set of good routes, and probabilistically chooses one of them. In this way, a different path is used at different times, so there is a balance in the energy of different nodes and the whole network lifetime increases. This is similar to Directed Diffusion, as data flows once again along different paths.
However, while Directed Diffusion constantly sends data along each path with different data rates, here only one path at a time is used.
Theprotocol comprises three different phases: setup, data communication, and route maintenance.
During the setup phase the routing tables are created, with all the routes from sources to destina-tions and the relative energy costs. Connecdestina-tions are initiated by the destination nodes by localized flooding, i.e., request packets are forwarded to all neighbors that are closer to the source node than the node itself. This mechanism requires a location subsystem on each node. When a request is received (at each hop), the total cost of the path is updated by adding the energy metric for the neighbor that sent the request. That is, if the request is sent from node Nito node Nj, Njcalculates the cost of the path as
CNj,Ni =Cost(Ni) +Metric(Nj, Ni). (.) Theenergy metric proposed in [Sha] from node Ni to node Nj is Ci j = eαi jRβi, where ei jis the energy used for transmitting and receiving on the link, Riis the normalized value for the residual energy, and α and β are two weighting factors.
Paths having a very high cost are discarded, while low-cost neighbors are added to the forwarding table on Nj. In the forwarding table, FTj, each neighbor is assigned a probability that is inversely proportional to the cost, that is:
PNj,Ni = /CNj,Ni
∑k∈FTj/CNj,Nk
. (.)
Then Nj can calculate the average cost to reach the destination by probabilistically choosing the neighbors in the forwarding table FTj, that is:
Cost(Nj) = ∑
i∈FTj
PNj,NiCNj,Ni. (.)
The average cost just calculated is set in the cost field of the request and forwarded toward the source node.
In the Data Communication phase of the protocol, each node simply forwards data packets to a random node in its forwarding table according to the stored probabilities, until the data packet
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reaches the destination. Route maintenance is very simple, as it just sporadically performs localized flooding in order to keep all the paths alive.
EAR requires a lower overhead than Directed Diffusion. Simulation results show that the improve-ment in power consumption is up to .%, while the network lifetime, here meaning the time when the first node runs out of energy, increases up to %. However, these results assume that energy is only consumed for packet transmission and reception, i.e., there is no energy consumption dur-ing the idle state. Thus, also in EAR, energy efficiency is accomplished by reducdur-ing the overhead.
While the routing protocol does not explicitly decrease either the duty cycle or collisions, the authors propose the design of a sensor node that follows the PicoRadio architecture [DaS], which features two radio transceivers, one always active but with a very low bit rate and power consumption, and the other with a low duty cycle and a higher data rate. With a similar node, CSMA/CA can be used in the low-power signalling channel in order to avoid collisions during data transmission.
7.4.4 Maximum Lifetime Routing Protocol
In [Cha] the routing problem in a WSN is modeled as a linear optimization problem. Instead of trying to minimize the energy of single transmissions or single paths, the objective is maximization of the lifetime of the overall sensor network. This work extends previous work by the same authors presented in [Cha] and [CT]. In these works it was found out that, in order to maximize network lifetime, it is more useful to balance the energy consumption between the nodes in proportion to their energy rather than minimize the power consumed by transmissions. The algorithm used to balance energy along the whole path is the Maximum Residual Energy Path. This algorithm always selects the path with the maximum residual energy to route packets across the network. Nodes can calculate the maximum residual energy path in several different ways. In [CT] the use of a path length vector is proposed, which works as follows: for each path from a source i and a destination d, a path length Lpis defined that is a vector of link costs cjk, where (j,k) is a link in the path. The value of cjkis the reciprocal of the residual energy at node j after the routing has been performed, i.e.,
Cjk=
Ej−ejk (.)
where
Ejis the residual energy at node j
ejkis the energy consumed to transmit over the link (j, k)
Using this formulation, the maximum residual energy path can be calculated as the shortest path with a slightly modified version of the Bellman–Ford algorithm [Bel] [For] which compares the entries of the path length vector in lexicographical order. In [Cha] an alternative way to calculate the maximum residual energy path is also provided, simply using the largest element of the path length vector for comparison. As an alternative, the link cost is proposed, which reflects the number of packets that can be delivered with the residual energy of the nodes, so the cost value is
Ci j= ei j
Ei. (.)
Theprotocol has been compared with the minimum transmitted energy (MTE) algorithm [She] (in which each node sends a message to the closest node on the way to the base station, BS) that uses ei jas the transmission cost. Simulation results show that the Maximum Residual Energy algorithm is more energy-efficient than MTE, as the network lifetime noticeably increases. Furthermore, the new metric is shown to perform better than the one presented in [CT], but the use of the maximum cost instead of the whole vector of path costs causes a slight performance degradation. So the best combination is the new metric combined with the old way to calculate the maximum residual energy path. However,
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both the algorithm and the simulations only consider energy consumption due to transmission, i.e., no idle energy consumption or the possible use of low-power/sleep states are taken into account.
While this protocol achieves interesting results from the theoretical point of view, in a real network there are also some drawbacks. For example, it lacks flexibility, as packets could be of variable size and the MAC layer could have some nondeterministic behavior, so it is not always simple to accurately estimate the energy required by each packet. In addition, this protocol requires topology knowledge, which in large WSNs is not simple to achieve.