AbstractEnergy harvesting devices are in rise now. These are devices that comes with low power batteries that can be easily charged with small solar cells. Typically the specification of such batteries are 50mWatt to 150mWatt. They are increasingly being used in sensor network as sensor nodes need to remain active for longer period of time. However charring of the sensor nodes is typically slower than the power usage due to excessive participation of the nodes in routing and sensing. Henc esuitable techniques should be adopted for both routing as well as data scheduling at the sensor nodes such that , nodes do not loose enough energy before they are charged. In this work we propose a comprehensive routing and scheduling scheme for energy harvesting sensor network that can optimize the power loss and increase the lifetime of the network. We adopt: 1. Minimum spanning tree based routing 2) Shortest path based routing and 3) Direct diffusion to obtain routes from different selected sensors to the sink node. Sensor nodes do not send the data immidiately when they sense. Rather they wait for specific period of time and then suitably schedule the data. We adopt a) Centralized scheduling using Linear Programming b) Decentralized scheduling using flow graph ratio over spanning tree. We compare all the techniques for energy loss and packet generated and prove that decentralized and distributed scheduling yeilds better result.

Chapter 1Preamble1.1 ObjectiveEnergy harvesting devices are typically low power. The intention behind using such devices is that the devices would not need to replace batteries frequently. However due to excessive operations by transmitter, receiver, sensor and processing circuits nodes loose more energy at a unit time than they acquire. Further acquisition of energy differs in different times of the day. Considering solar nodes, nodes charges faster at the day time than in nights. Therefore nodes must adopt suitable techniques that enables them to restore their energy even when they are not charging by suitably reducing the packet transmission and routing participation. Therefore in this work our objective is to develop an efficient solution for energy harvesting sensor network over different topologies that can easily sustain the gained energy throughput the day. The object is also to analyze the concept for different routing techniques and for diferent scheduling methods.1.2 Statement of the Problem

Conventional sensor network works proposes lifetime maximization through different approaches like clustering and wise routing. However energy harvesting nodes beg to differ from the conventional sensor nodes in a way that they are low powered. Thus convention techniques do not offer ample solution for the same. Therefore in this work we develop centralized and distributed scheduling techniques for sensor network and test them for most adopted routing techniques for sensor nodes. We prove that using decentralized technique improves the network performance in terms of low energy dissipation and thus improve lifetime.

1.3 Scope of the WorkMore and more devices are now powered with alternative energy sources like wind power, solar energy and so on. Charging of the cells takes longer time when power rating is higher. Therefore such devices are lower power. Current work solves the puzzle of retaining maximum energy of the nodes over a longer period of time and eliminate the need of battery replacement in the nodes. Also using proper scheduling techniques can avoid network congestion which results in lower number of retransmission. Therefore nodes do not loose energy for overhearing or packet congestion. The work is also suitable to define routing structure in the network. It can also be used for data aggregation in sensor network and design a suitable scheduling for data aggregation.

1.4 LimitationScheduling techniques generally introduces delay in transmission. hence latency gets increased. Some of the sensor networks are deployed for mission critical applications like flood alarm. Current protocol can not be used for such networks.

Chapter 2General Introduction2.1 Basics of Sensor Network2.2 Routing in Sensor NetworkDirected Diffusion Directed diffusion works by disseminating sensor tasks throughout the sensor network as an interest for named data. The dissemination of the interest sets up time stamped gradients in the network designed to draw data about events back to the originating node (the sink). When sensors detect events matching the request the event is sent towards the originator along multiple paths. The stored gradients give a direction and data rate and are used to determine the paths to be taken. Initially the sink requests a low data rate from the network. Once the sink begins to receive data back from the network it reinforces one or more paths with a higher data rate. The interest is periodically refreshed with a new time stamp by the sink to keep the interest up to date.

Minimum Spanning TreeEvery computer program is based upon some kind of algorithms. Efficient algorithms will produce efficient programs. Let us consider the minimal spanning tree problem. We believe that the reader will quickly understand why it is so important to study algorithm design. There are two versions of spanning tree problem. We will introduce one version of them first. Consider Figure 2.1.

Figure 2.1: A Set of Planar Points

A tree is a graph without cycles and a spanning tree of a set of points is a tree consisting all of the points. In Figure 2.2, we show three spanning trees of the set of points in Figure 2.1.

Figure 2.2: Three Spanning Trees of the Set of Points in Figure 2.1

Among the three spanning trees, the tree in Figure 2.2(a) is the shortestand this is what we are interested. Thus the minimal spanning tree problem is defined as follows: We are given a set of points and we are asked to find a spanning tree with the shortest total length.How can we find a minimal spanning tree? A very straightforward algorithm is to enumerate all possible spanning trees and one of them must be what we are looking for. Figure 2.3 shows all of the possible spanning trees for three points. As can be seen, there are only three of them.

Figure 2.3: All Possible Spanning Trees for Three Points

For four points, as shown in Figure 2.4, there are sixteen 16 possible spanning trees. In general, it can be shown that given n points, there are possible spanning trees for them. Thus if we have 5 points, there are already = 125 possible spanning trees. If n = 100, there will be possible spanning trees. Even if we have an algorithm to generate all spanning trees, time will not allow us to do so. No computer can finish this enumeration within any reasonable time.

Figure 2.4: All Possible Spanning Tree for Four Points

Yet, there is an efficient algorithm to solve this minimal spanning tree problem. Let us first introduce the Prim's Algorithm.

Prim's Algorithm

Consider the points in Figure 2.1 again. Suppose we start with any point, say point b. he nearest neighbor of point b is point a. We now connect point a with point b , as shown in Figure 2.5(a). Let us denote the set as X and the set of the rest of points as Y. We now find the shortest distance between points in X and Y which is that between between b and e, We add e to the minimal spanning tree by connecting b with e, as shown in Figure 2.5(b). Now X = and Y = . During the whole process, we continuously create two sets, namely X and Y. X consists of all of the points in the partially created minimal spanning tree and Y consists of all of the remaining points. In each step of Prim's algorithm, we find a shortest distance between X and Y and add a new point to the tree until Y is empty. For the points in Figure 2.1, the process of constructing a minimal spanning tree through this method is shown in Figure 2.5.

Figure 2.5: The Process of Constructing a Minimal Spanning Thee Based upon Prims Algorithm

In the above, we assumed that the input is a set of planar points. We can generalize it so that the input is a connected graph where each edge is associated with a positive weight. It can be easily seen that a set of planar points corresponds to a graph where there is an edge between every two vertices and the weight associated with each edge is simply the Euclidean distance between the two pints. If there is an edge between every pair of vertices, we shall call this kind of graph a complete graph. Thus a set of planar points correspond to a complete graph. Note that in a general graph, it is possible that there is no edge between two vertices. A typical graph is now shown in Figure 2.6.Throughout the entire book, we shall always denote a graph by G = (V,E) where V is the set of vertices in G and E is the set of edges in G.

Figure 2.6: A General Graph

We now present Prim's algorithm as follows:

Algorithm 2.1 Prim's Algorithm to Construct a Minimal Spanning TreeInput: A weighted, connected and undirected graph G = (V,E).Output: A minimal spanning tree of G.

Step 1: Let x be any vertex in V. Let X = and Y = V \

Step 2: Select an edge (u,v) from E such that ,and (u,v)has the smallest weight among edges between X and Y.

Step 3: Connect u to v. Let and .Step 4: If Y is empty, terminate and the resulting tree is a minimal spanning tree. Otherwise, go to Step 2.

Let us consider the graph in Figure 2.7. he process of applying Prim's algorithm to this graph is now illustrated in Figure 2.8.

Figure 2.7: A General Graph

Figure 2.8: The Process of Applying Prims Algorithm to the Graph in Figure 2.7

We will not formally prove the correctness of Prim's algorithm. The reader can find the proof in almost every textbook on algorithms. Yet, now we can easily see the importance of algorithms. If one does not know the existence of such an efficient algorithm to construct minimal spanning trees, one can never construct minimal spanning trees if the input size is large. It would be disastrous for any one to use an exhaustive search method in this case.In the following, we will introduce another algorithm to construct a minimal spanning tree. This algorithm is called Kruskal's algorithm.

Kruskal's Algorithm

Kruskal's algorithm to construct a minimal spanning tree is quite similar to Prim's algorithm. It would first sort all of the edges in the graph into an ascending sequence. Then edges are added into a partially constructed minimal spanning tree one by one. Each time an edge is added, we check whether a cycle is formed. If a cycle is formed, we discard this edge. The algorithm is terminated if the tree contains n-1 edges.

Algorithm 2.2 Kruskal's Algorithm to Construct a Minimal Spanning TreeInput: A weighted, connected and undirected graph G = (V,E).Output: A minimal spanning tree of G.

Step 1: .Step 2: while T contains less than n-1edges doChoose an edge (v,w) from E of the smallest weight.Delete (v,w) from E.If the adding of (v,w)does not create cycle in T thenAdd (v,w) to T.Else Discard (v,w).end while

Let us consider the graph in Figure 2.7. The process of applying Kruskal's algorithm to this graph is illustrated in Figure 2.9.Both algorithms presented above are efficient.

Figure 2.9: The Process of Applying Kruskals Algorithm to the Graph in Figure 2.7

Flooding In the flooding protocol each node receiving a data or management packet repeats the packet by broadcasting it. Only packets which are destined for the node itself or packets whose hop count has exceeded a preset limit are not forwarded.

The main benefit of flooding is that it requires no costly topology maintenance or route discovery. Once sent a packet will follow all possible routes to its destination. If the network topology changes sent packets will simply follow the new routes added. Flooding does however have several problems. One such problem is implosion. Implosion is where a sensor node receives duplicate packets from its neighbors. Figure 2.3a illustrates the implosion problem. Node A broadcasts a data packet ([A]) which is received by all nodes in range (nodes B and C in this case). These nodes then forward the packet by broadcasting it to all nodes within range (nodes A and D). This results in node D receiving two copies of the packet originally sent by node A. This can result in problems determining if a packet is new or old due to the large volume of duplicate packets generated when flooding. Overlap is another problem which occurs when using flooding. If two nodes share the same observation region both nodes will witness an event at the same time and transmit details of this event. This results in nodes receiving several messages containing the same data from different nodes. Figure 2.3b illustrates the overlap problem. Nodes A and B both monitor geographic region Y. When nodes A and B flood the network with their sensor data node C receives two copies of the data for geographic region Y as it is included in both packets. Another problem with flooding is that the protocol is blind to available resources. Messages are sent and received by a node regardless of how much power it has available. In addition to this the number of packets generated by the flooding protocol causes a lot of network traffic and causes a large network wide energy drain across the network. This can shorten the life of the network.

Figure 2.3a: The Implosion problemFigure 2.3b: The Overlap problem

Dijkstra's Algorithm

This algorithm finds the shortest path from a source vertex to all other vertices in a weighted directed graph without negative edge weights.

Here is the algorithm for a graph G with vertices V = {v1, ... vn} and edge weights wij for an edge connecting vertex vi with vertex vj. Let the source be v1.

Initialize a set S = . This set will keep track of all vertices that we have already computed the shortest distance to from the source.

Initialize an array D of estimates of shortest distances. D[1] = 0, while D[i] = , for all other i. (This says that our estimate from v1 to v1 is 0, and all of our other estimates from v1 are infinity.)

While S != V do the following: 1) Find the vertex (not is S) that corresponds to the minimal estimate of shortest distances in array D.2) Add this vertex, vi into S.3) Recompute all estimates based on edges emanating from v. In particular, for each edge from v, compute D[i]+wij. If this quantity is less than D[j], then set D[j] = D[i]+wij.

Essentially, what the algorithm is doing is this:

Imagine that you want to figure out the shortest route from the source to all other vertices. Since there are no negative edge weights, we know that the shortest edge from the source to another vertex must be a shortest path. (Any other path to the same vertex must go through another, but that edge would be more costly than the original edge based on how it was chosen.)

Now, for each iteration, we try to see if going through that new vertex can improve our distance estimates. We know that all shortest paths contain subpaths that are also shortest paths. (Try to convince yourself of this.) Thus, if a path is to be a shortest path, it must build off another shortest path. That's essentially what we are doing through each iteration, is building another shortest path. When we add in a vertex, we know the cost of the path from the source to that vertex. Adding that to an edge from that vertex to another, we get a new estimate for the weight of a path from the source to the new vertex.

This algorithm is greedy because we assume we have a shortest distance to a vertex before we ever examine all the edges that even lead into that vertex. In general, this works because we assume no negative edge weights. The formal proof is a bit drawn out, but the intuition behind it is as follows: If the shortest edge from the source to any vertex is weight w, then any other path to that vertex must go somewhere else, incurring a cost greater than w. But, from that point, there's no way to get a path from that point with a smaller cost, because any edges added to the path must be non-negative.

By the end, we will have determined all the shortest paths, since we have added a new vertex into our set for each iteration.This algorithm is easiest to follow in a tabular format.

The adjacency matrix of an example graph is included below. Let a be the source vertex.

abcdea010 infinf3binf082infc2304infd5 inf40infeinf1216130

Here is the algorithm:

EstimatesbcdeAdd to Seta10 infinf3e1019163b1018123d1016123

We changed the estimates to c and d to 19 and 16 respectively since these were improvements on prior estimates, using the edges from e to c and e to d. But, we did NOT change the 10 because 3+12, (the edge length from e to b) gives us a path length of 15, which is more than the current estimate of 10. Using edges bc and bd, we improve the estimates to both c and d again. Finally using edge dc we improve the estimate to c.

Now, we will prove why the algorithm works. We will use proof by contradiction. After each iteration of the algorithm, we "declare" that we have found one more shortest path. We will assume that one of these that we have found is NOT a shortest path.

Let t be the first vertex that gets incorrectly placed in the set S. This means that there is a shorter path to t than the estimate produced when t is added into S. Since we have considered all edges from the set S into vertex t, it follows that if a shorter path exists, its last edge must emanate from a vertex outside of S to t. But, all the estimates to the edges outside of S are greater than the estimate to t. None of these will be improved by any edge emanating from a vertex in S (except t), since these have already been tried. Thus, it's impossible for ANY of these estimates to ever become better than the estimate to t, since there are no negative edge weights. With that in mind, since each edge leading to t is non-negative, going through any vertex not in S to t would not decrease the estimate of its distance. Thus, we have contradicted the fact that a shorter path to t could be found. Thus, when the algorithm terminates with all vertices in the set S, all estimates are correct.

Try an example your self on the graph with the following adjacency matrix using a as the source.

abcdea03 4infinfbinf0inf68cinf2015dinf infinf02einfinfinfinf0

Path Reconstruction in Dijkstra's Algorithm

Given the history of the values in the distance estimate array, we can trace the shortest path. In the example below, consider determining the shortest distance from a to c:

EstimatesbcdeAdd to Seta10 infinf3e1019163b1018123d1016123

When we updated the estimate to c to be 16, we added vertex d. This means that the last edge of the path was d -> c. Now, go to the column for d. We see that d's distance was updated when we added b to the set S. Thus, the last edge of the shortest path from a to d is the edge d->b. Finally, we go to the column for b and find that the shortest distance to b is obtained by the edge a->b. Thus, putting everything together, we have the path a->b->d->c.

Consider if we had filled out the chart as follows:

EstimatesbcdeAdd to Seta10/a infinf3/ae10/a19/e16/e3/ab10/a18/b12/b3/ad10/a16/d12/b3/a

When we update a estimate, we ALSO update which vertex got us there. When we finish, we just need the information on the last row to reconstruct any shortest path from a.2.3 Energy Harvesting sensor networkWireless distributed microsensor systems will enable fault tolerant monitoring and control of a variety of appli-cations. Due to the large number of microsensor nodes that may be deployed and the long required system lifetimes, replacing the battery is not an option. Sensor systems must utilize the minimal possible energy while operating over a wide range of operating scenarios. This paper presents an overview of the key technolo-gies required for low-energy distributed microsensors.These include power aware computation communication component technology, low-energy signaling and networking, system parti-tioning considering computation and communication trade-offs, and a power aware software infrastructure. The design of micropower wireless sensor systems has gained increasing importance for a variety of civil and military applications. With recent advances in MEMS technology and its associated interfaces, signal processing, and RF circuitry, the focus has shifted away from limited macrosensors communicating with base stations to creating wireless networks of communicating microsensors that aggregate complex data to provide rich, multi-dimensional pictures of the environment. While individual microsensor nodes are not as accurate as their macrosensor counterparts, the networking of a large number of nodes enables high quality sensing networks with the additional advantages of easy deployment and faulttolerance. These characteristics that make microsensorsideal for deployment in otherwise inaccessible environmentswhere maintenance would be inconvenient or impossible. The potential for collaborative, robust networks of microsensors has attracted a great deal of research attention. The WINS and PicoRadio and projects, for instance, aim to integrate sensing, processing and radio communication onto a microsensor node. Current prototypes are custom circuit boards with mostly commercial, off-the-shelf components. The Smart Dust project seeks a minimum-size solution to the distributed sensing problem, choosing optical communication on coin-sized motes. The prospect of thousands of communicating nodes has sparked research into network protocols for information flow among microsensors, such as directed diffusion [7].The unique operating environment and performance requirements of distributed microsensor networks require fundamentally new approaches to system design. As an example,consider the expected performance versus longevity of the microsensor node, compared with current battery-powered portable devices.

Fig1.1-wireless sensor network The node, complete with sensors, DSP, and radio, is capable of a tremendous diversity of functionality.Throughout its lifetime, a node may be called upon to be a data gatherer, a signal processor, and a relay station. Its lifetime, however, must be on the order of months to years, since battery replacement for thousands of nodes is not an option. In contrast, much less capable devices such as cellular telephones are only expected to run for days on a single battery charge. High diversity also exists within the environment and user demands upon the sensor network. Ambient noise in the environment, the rate of event arrival, and the users quality requirements of the data may vary considerably over time. A long node lifetime under diverse operating conditions demands power-aware system design. In a power-aware design, the nodes energy consumption displays a graceful scalability in energy consumption at all levels of the system hierarchy, including the signal processing algorithms, operating system, network protocols, and even the integrated circuits themselves. Computation and

Fig-wireless sensor network with gateway sensor nodecommunication are partitioned and balanced for minimum energy consumption. Software that understands the energy-quality tradeoff collaborates with hardware that scales its own energy consumption accordingly.

Chapter 3Related Work

Chapter 4System Analysis4.1 Present System Enenrgy harvesting sensor network is relatively new concept and not many proposals have suitably offered solutions for this. Most of the conventional techniques offers wither 1) An energy aware route 2) Cluster based Solutions and 3) Scheduling TechniquesExisting work had the objective of minimizing the completion time of converge casts. However, none of the previous work discussed the effect of multi-channel scheduling together with the comparisons of different channel assignment techniques and the impact of routing trees and none considered the problems of aggregated and raw converge cast, which represent two extreme cases of data collection,

4.2 Proposed SYstemEven though the main objective here is to analyze the effect of Centralized and decentralized scheduling, we emphesize on appropriate scheduling techniques over tree based network. i.e. Scheduling for minimum spanning tree.Many wireless sensor networks (WSNs) employ battery-powered sensor nodes. Communication in such networks is very taxing on its scarce energy resources. Convergecast process of routing data from many sources to a sink is commonly performed operation in WSNs. Data aggregation is a frequently used energy-conversing technique in WSNs. The rationale is to reduce volume of communicated data by using in-network processing capability at sensor nodes. In this paper, we address the problem of performing the operation of data aggregation enhanced convergecast in an energy and latency efficient manner. We assume that all the nodes in the network have a data item and there is an a priori known application dependent data compression factor (or compression factor), c, that approximates the useful fraction of the total data collected.One is a variant of the Minimum Spanning Tree (MST) algorithm and the other is a variant of the Single Source Shortest Path Spanning Tree (SPT) algorithm. These two algorithms serve as a motivation for our Combined algorithm which generalized the SPT and MST based algorithm.The algorithm tries to construct an energy optimal tree for any fixed value of a (= 1 _ c), the data growth factor. The nodes of these trees are scheduled for collision-free communication using a channel allocation algorithm. To achieve low latency, these algorithms use the b-constraint, which puts a soft limit on the maximum number of children a node can have in a tree. The tree obtained from energy minimizing phase of tree construction algorithms is restructured using the b-constraint (in the latency minimizing phase) to reduce latency (at the expense of increasing energy cost). The effectiveness of these algorithms is evaluated by using energy efficiency, latency and network lifetime as metrics. With these metrics, the algorithms performance is compared with an existing data aggregation technique. From the experimental results, for a given network density and data compression factor c at intermediate nodes, one can choose an appropriate algorithm depending upon whether the primary goal is to minimize the latency or the energy consumption.

Chapter 5

Spanning TreeSystem Design

Scheduling Control SinkLP Based SchedulingFlow Based SchedulingEnergy HarvestingData AcquiringDirected Diffusion, FloodingShortest PathSensor nodes

5.2 DLEG5.3 DLEX5.4 ImplementationExplain Matlab code Here

Chapter 6Results and Discussion

Chapter 7Conclusion