International Journal of Wireless Communications and Networking 3(1), 2011, pp. 7-13 CACHING IN WIRELESS SENSOR NETWORKS BASED ON GRIDS Sudhanshu Pant 1, Naveen Chauhan 2 and Brij Bihari Dubey 3 Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, India E-mail: 1 sudhanshupant1986, 2 naveenchauhan.nith, 3 dubey.brijbihari}@gmail.com Abstract: Cooperative caching is important to many wireless sensor applications. This paper exploits the cooperative caching and proposes an energy efficient approach to deal with the network traffic in wireless sensor networks. Cooperative caching is done in the form of concentric circular cache layers around the sink. Circular cache layer is a group of nodes falls under circumference of the circle formed from the sink as center with a certain radius. Each sink develops the circular cache layer around it to cache the data items. To increase network lifetime a grid based approach is given which dynamically forms the grids to distribute load throughout the network. The scheme performs well in the multiple and mobile sink environment. We design a simple scheme to deal with the sink mobility which requires very less overheads and sink nodes get easily adapted into the new location. Proposed scheme is compared with Dual Radio based Data Dissemination (DRDD) approach. By means of proper simulation the presented scheme performs better than, when no caching is applied to the network. Keywords: Caching, Cooperative caching, Circular Cache layer, WSN, Energy Efficiency. I. INTRODUCTION The development of sensor nodes equipped with flash memory is giving new direction in designing and deploying the energy efficient Wireless Sensor Network. A large scale sensor network consists of thousands of sensor nodes distributed over a vast field [1]. All the sensor nodes are allowed to communicate through a wireless medium. In a WSN the node that gathers the data information refers to sink. Providing continuous information to mobile sinks with uninterrupted communication is a big challenge in designing large-scale sensor networks. A lot of research in data routing [2], data compression [3] and in-network aggregation [4] has been proposed in recent years. Caching if implemented optimally can reduce a lot of network traffic and helps in providing higher data availability to the users (sink). In this paper a new data caching technique is proposed which caches data nearer to the sink. The optimal way to cache data near to the sink is the circular layered area around the sink. Our scheme caches data in the nodes surrounded by the sink in the circular cache layers. Each concentric circle forms a cache layer and the data gets cached in these circular cache layers starting from the innermost circle. Each concentric circular cache layer gets a token which decides which cache layer will caches the data. The Circular Cache layers (CCL) are formed by the Circular Cache Formation (CCF) algorithm. The algorithm searches for the geographical coordinates of the nodes around the sink, by flooding request messages, to determine there distance form the sink. The nodes which fall under a particular distance from the sink forms a cache layer. The discovery of cached data is operated by a simple cache discovery scheme. Finally, a data replacement policy is given which helps in removing obsolete data from the caches. The sensor field is organized in the form of a grid. The grid is formed in the form of square cells. Each square cell may have four dissemination nodes at its four corners. A request from the sink may include one of these four dissemination nodes in its path to the sink. The multiple sink could be present at any place in the sensor field. Rest of the paper is organized as follows. Section II describes the related work done so far in the cooperative caching. In section III, the concept of proposed scheme is discussed without giving the details of how the scheme works. Section IV gives the explanation of the presented scheme describing query and data processing within the sensor nodes. Section V gives the simulation and analysis including comparison analysis and results.
8 International Journal of Wireless Communications and Networking Conclusion out of exploiting the proposed scheme is discussed in Section VI. II. RELATED WORK Several works has been proposed by the authors exploiting caching the data either in some intermediate nodes or at a location nearer to the sink in the Wireless Sensor Networks. Indeed providing solutions to optimally caching the data has been a big area to be focus on, several proposed schemes performs well. Jinbao Li et al. [5] proposes a caching scheme for the multi-sink sensor network. The sensor network forms a set of network trees per sink. The common subtree is formed out of these set and the root of the common subtree is selected as the data caching node to reduce the communication cost. GroCoCa [6] suggest a data item to be cached depends on two factors of the access affinity on the data items and the mobility of each node. Depending upon the mobility pattern of the mobile nodes they are tightly coupled to form a group. The Cluster Cooperative protocol [7] given by N. Chand et al. attempts to form non-overlapping clusters based in geographical proximity. The idea is to partition whole Mobile Adhoc Network (MANET) into equal size cluster and for a cache miss in the local cache of the node, each client looks for the data item in the cluster. J. Xu et al. [8] gives a waiting cache scheme which waits for the data of the same cluster until it becomes available within a threshold, aggregating it with the packet from the lower cluster and then sending it to the sink, thus reducing number of packets from the network. Figure 1: Typical Setup of Wireless Sensor Network III. SYSTEM ENVIRONMENT A. Network Model Our scheme assumes Sensor field comprises of large number of sensor nodes capable of communicating each other through wireless medium. Each sensor nodes is aware of there geographical coordinates (x, y), which are also the node identities. The grid formation used here is same as proposed by Luo et al. [9]. The grid formation occurs periodically such that energy consumption could be spread throughout the whole network. The Periodic Grid Formation (PGF) algorithm checks for this constraint and update the grids according to certain parameters. Although Luo et al. insists on making grid on per-source basis, PGF constructs only one grid at a time for the whole sensor network field. The grid diagonals are selected as proposed by T. P. Sharma et al. [10]. The sensor nodes are assumed to have two working modes low power and high power ranges. The grid diagonals are selected as the high power transmission range. The dissemination between the two cells is done using the dissemination nodes at the corner. Several issues in constructing grids are there and are discussed in the following sections. A typical setup of the sensor network is shown in figure 1. The figure 1 shows two sinks are requesting data from two different sources. The CCLs of two sinks may overlap. In such case the overlapped node is a part of both sink cache. The communication path setups for the two sinks are shown by the arrows. The active cache layer is shown with the dark circles. B. Cooperative Caching Circular Cache Layers are utilized to cache the data from the sources. The CCLs are the concentric circles of sensor nodes with the sink as centre and data gets stored starting from the centre and then to the next CCL taking one at a time. The CCF algorithm builds the CCLs around the sink. The CCF algorithm forms the cache circular layers for each and every sinks and the CCL having the token will cache the data in it. As soon as the cache of all the nodes in the active CCL becomes full the token is passed to the next successive CCL. Figure 2 depicts the formation of cache circular layers. The radii for two adjacent cache layers are selected as per the size of sensor node. For simplicity we consider circular sensor node and cache circular layers are formed according to the size of the sensor nodes.
Caching in Wireless Sensor Networks based on Grids 9 3: Construct a new grid starting from a node having energy much larger than Y. 4: end if 5: Repeat Step 1 after a certain time interval. Threshold range can be chosen as a range around the half of the node s energy. The effect of taking various threshold ranges will be discussed in the analysis part later. Till this point we may assume the threshold range between X J to Y J, i.e., Figure 2: Formation of Circular Cache Layers IV. QUERY AND DATA PROCESSING This section presents the design of the caching scheme. The whole process occurs in following steps: (1) Initially, any source sensing an event starts forming the grid same as described in [9]. For whole sensor network only one grid is formed at a time. (2) Each sink builds there CCL around its location. The CCF algorithm builds the CCLs for each and every sink. (3) Sink queries the required data through flooding the request around it. (4) The requested data gets provided to the sink either by the cache or directly through the source if there is a cache miss due to cache invalidation or a fresh request. The descriptions of these steps are further elaborated below. A. Periodic Grid Formation (PGF) Algorithm The PGF algorithm presented here, periodically checks the node energy of the corner dissemination nodes in the cells of the grid formation. The node status can be determined by the beacon messages. The idea is to check corner DNs energy and when the energy of some of these DNs falls in a threshold range, PGF algorithm again construct a new grid taking the starting node of grid formation as that node having the energy larger than the highest energy value in the threshold range. Periodical Grid Formation (PGF) Algorithm: 1: Retrieve the node s energies of DNs 2: If number of DNs = an upper_bound falls under the threshold range X threshold range Y (1) The value of upper_bound suggests how many nodes should falls under this range so that the network will remain active as long as possible. An optimal value of upper_ bound saves a lot of overall network energy consumption. B. Circular Cache Layer The CCLs are formed as per the specified radius of the concentric circles in the CCF algorithm. The sink itself is considered as layer 0 cache. Layer 1 circular cache is formed by distributing message identifying the radius R1. All the nodes covering radius R1 and not including the layer 0 node, falls under the layer 1 cache. Similarly all the nodes covering radius R2 and not including the layer 0 and layer 1 cache nodes forms the layer 2 cache and so on. To store the data items, initially layer 0 cache becomes active and called as Active Cache Layer (ACL). To activate an ACL a token is passed to all the nodes of the ACL. As soon as all the cache becomes full, the token is passed to next successive layer and that layer becomes the ACL. By the time the highest CCL will get full, some entries in the lower level CCL may get free due to time-to-live value associated with each data items. After filling up all the nodes in the last layer, the next layer which becomes ACL is layer 0 then layer and so on. In this round the nodes which are free gets filled. If no nodes are found free then a cache invalidation policy is needed to replace the data items. The cache invalidation rules are discussed later in this section in cache management scheme. Each layers of cache consist of increasingly varying number of nodes. Not all nodes in the active cache layer have free cache memory. Rather than waiting for the total number of nodes in cache layer to become free, we give emphasis on the amount of free available cache in the layer in question.
10 International Journal of Wireless Communications and Networking Circular Cache Formation (CCF) Algorithm: 1: for i from 1 to max_cache_layer 2: sink floods message with radius = R i and sink co-ordinates (x, y) 3: node n (x n, y n ) receives the message 4: if {(x n x) 2 + (y n y) 2 } R i && n!= lower layer node. 5: mark n as layer i node. 6: forward the received message. 7: else discard message. 8: end if 9: end for The max_cache_layer is the measure of the number of queries generated per unit time interval and the size of the cache. If m queries are generated per unit time and it needs an average of n Bytes to store the query reply then the sensor network is producing m*n Bytes of data per unit time. Figure 2 gives an idea about how radius of particular CCL is selected. For building two consecutive CCLs we must have d > R n -R n-1 2d (2) This value is taken so as to make efficient CCLs. The maximum number of nodes that can be in a layer n can be given as: N = K * 4 * [R n 2 R n-12 ]/d (3) where, 0 < K 1 and d denotes the approximate physical diameter of a sensor node, considering nodes are spherical in shape. The value of K denotes the density of nodes. We can set max_cache_layer parameter by using this theoretical result. The maximum numbers of cache layers are analyzed more in analysis and simulation part. C. Query Processing To make a query, sink floods the query request message to its cache layers one by one. If the data item is found in first layer, it is retrieved from that layer node. Otherwise the query request message is passed to next layer. The query may arrive at a cached sensor node in the higher cache layer and the sensor node will reply with the cached results. If none of the cache layer has the desired data then the query is flooded into the whole network, of course in the forward direction, to retrieve the desired result directly from the source node. The sensor node reply using the high power transmission range, same as described in the next case, so that the response time will be less. If the data is not found in any of the CCLs of the sink node then the query reaches to the source node and source node will reply as follows: (1) If the source node is the corner dissemination node of the grid cell, then the source directly reply to the opposite dissemination node i.e. opposite to the path of query request, using the high power transmission range. The high power transmission reduces the number of hops and hence reduces the response time of the query. (2) If the source node is not the corner dissemination node, then it may be a node inside the cell. The source node replies the query request to the corner DN in the path of the query request using the high power transmission range. The DN then forwards this reply messages towards the sink same as described in previous case. Each query request message contains the identity of the active cache layer in its header field. When the request is replied back to the sink it gets cached in the active cache layer also. PGF algorithm checks for the nodes energies and forms a new grid if required. D. Sink Mobility Each node knows in which cell it is present. If the node is moving in its present cell, there is no requirement of changing the protocol. This is because the DN directly communicates with the sink and DN s high power range could easily covers the area of that particular cell. Moreover for a particular cell, when new query is generated by the sink, it just floods the request message to the surrounding nodes and the cached data can be easily available. So there is no need of redesigning the CCL. But when node moves long, moves from one cell to another, the cached nodes becomes far away from the sink. In this case the sink again calls the CCF algorithm to make new CCL around it. Thus, the CCF algorithm will be called only when a node crosses a cell boundary and not when PGF algorithm builds new grid. The sink again makes a query request and the new path may be forms as a result of this query, as the cell from which node queried the data has been changed. An advantage of this scheme is that when new CCLs get selected they might have required cached data items which were part of CCLs from previous location of that sink.
Caching in Wireless Sensor Networks based on Grids 11 E. Cache Management As there could be multiple sinks in the sensor network, a node could be involved in caching the data for more than one sink. Each node maintains the information about various caches layer to which it belong for the corresponding sinks. There is a Time to Live (TTL) value associated with each data which denotes its expiry time. This is the responsibility of the source to set this TTL value with each data. The source sets this value by calculating the frequency of updates in the data. The idea is to set larger value if frequency of updating is low and smaller values for the data where frequency of updating in high. Simulation has shown to increase in performance by using the above mentioned strategy. If it is required to invalidate the data entries from the cache due to lack of space then following rules are applied. Access Frequency (AF): The access frequency gives the measure of the number of times the data was accessed per unit time. Time-to-Live: The item with shorter TTL remains valid for shorter period is the best item to replace. But it may have higher access frequency so it is likely that this data item will further get accessed many times in future. Based on this factor, the product AF*TTL give the parameter to decide which item should be replaced. Distance from the source (D): If the distance is less from the source node then the query request will have to go very short and thus saves time and network traffic as compared to distant node. So the item with minimum distance from the source node is the beat option to replace. Based on above parameters the importance of data item is computed as follows: Imp i = AF * TTL * D (4) The item with the least Imp i value will get replaced if it is required to replace a data item from the sensor nodes cache. V. SIMULATION AND ANALYSIS We have simulated the scheme in ns-2 (version 2.34). In this section we evaluate the performance of our scheme. A. Simulation Parameters Sensor field is region of 1000 * 1000 m 2, with 200 sensor nodes deployed in it randomly. The sensor nodes can be in one of these modes: sleeping, sending message, receiving message. The diagonals are selected as high power transmission range of the sensor node i.e. R H =100 m whereas R L =25 m. For simplicity, sizes of both query and data packets are taken as 64bytes and energy parameters for node are as follows: ET High =0.0024 J, ET Low =0.0010 J, ER High =0.0018 J and ER Low =0.0008 J, where ET High and ET Low are energies consumed in transmitting a data/ query message to one hop neighbor in high power mode and low power mode respectively, and ER Low and ER High are energies consumed in receiving a data/query message from one hop neighbor in high power and low power modes respectively. The power consumption during the sleeping mode is set at 0.016 mw. The mobile sinks can have maximum speed of 10 m/s. The sensor nodes are equipped with the flash memory of 128 kb. B. Results and Analysis (1) Effect of Threshold Range and Upper Bound The threshold ranges are taken nearer to the half of the maximum nodes energy. The optimal value of the upper bound obtained is around 25. This value is calculated by setting threshold range gap of about 20. For threshold range gap below 10 and above 30, the energy consumption becomes higher under the set of parameters we have taken. When the gap will be less then PGF algorithm makes new grid very frequently and hence consumes more power in grid formation and consumes time. Considering large threshold range gap allows PGF algorithm to wait and some of the nodes which are left with less energy i.e. close to X, will loose more energy during this waiting and hence degrades overall performance. The scheme performs well when this gap is between 10 and 30. Similarly the upper bound for the number of dissemination nodes discussed in section IV (A) comes out to perform well when taken between 20 to 30 nodes. The optimal combination of these two parameters shows a saving of 38% in energy consumption. (2) Effect of Number of Queries For lesser value of Mean query generate time, the queries generated per unit time are more and hence consumes more power. To compare with DRDD
12 International Journal of Wireless Communications and Networking [10], the mean query generate time is taken. As shown in figure 3 Circular Cache scheme perform better than the DRDD and saves upto 45% energy consumption due to more number of cache hits than DRDD. (3) Effect of Cache Layers and Cache Size The number of cache layers greatly affects the number of hits in the CCLs. Figure 4(a) shows the number of hits when taking different numbers of cache layers. Initially Circular Cache scheme shows less number of cache hit due to less number of nodes involved in caching. As the number of cache layers increases more number of nodes gets involved in caching and hence results in large number of cache hits. The number of cache hits increases as the number of cache layers increases. More number of cache layer i.e., above a certain level, may results in non-increasing cache hits due to the fact that the sink doesn t generate more queries beyond a certain number. Figure 4(a): Effect of No. of Cache Layers on Cache Hits Figure 4(b): Effect of Cache Size on Number of Cache Hits DRDD. Circular Caching scheme shows 62% energy saving as compared to the scenario when no caching is used. DRDD shows up to 55% energy saving compared to when no caching is used. Figure 3: Effect of Mean Query Generate Time on Energy Consumption Figure 4(b) shows the number of hits in DRDD as well as Circular Cache scheme. The number of cache hits increase with the increasing number of data items that can be stored in a cache of a sensor node. After a certain level the cache hits starts reducing due to the invalidation strategies of data items. The figure 4(b) shows that Circular Cache results in better cache hits as it accumulates larger cache than the DRDD scheme. The circular cache scheme shows less energy consumption than VI. CONCLUSION In this paper technique that uses Circular Cache Layers to improve the performance of the Wireless Sensor Networks is discussed. The Circular Cache Layers provides the data items nearer to the sink and thus reduce the response time of the queries. The query results get stored in the active cache layer before reaching to the sink. We can make an energy efficient WSN only when the load is equally distributed in the network so that all the nodes consumes power equally and network becomes operational as long as possible. The proposed caching scheme can further be enhanced to increase the network performance. Simulation results show the overall network efficiency by means of various graphs. The proposed works is shown to perform well if applied practically in real world scenario under particular situations by the means of proper simulations.
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