PREDICTING NUMBER OF HOPS IN THE COOPERATION ZONE BASED ON ZONE BASED SCHEME

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1 44 PREDICTING NUMBER OF HOPS IN THE COOPERATION ZONE BASED ON ZONE BASED SCHEME Ibrahim Saidu a, *,Idawaty Ahmad a,b, Nor Asila Waty Abdul Hamid a,b and Mohammed waziri Yusuf b a Faculty of Computer Science and Information Technology, University Putra,Malaysia. b Faculty of Science, Department of Mathematics, UPM. Abstracts Due to the unpredictable mobility behaviours of nodes in mobile ad hoc networks, predicting the average number of hops becomes very essential in multihop ad hoc networks, which is used as a key metric in predicting the number of hops in cooperation zone. However, most current research derives the average number of hops based on simulations, lacking the mathematical analysis of this essential metric. This research presents a mathematical study of the number of hops in the cooperation zone based on zone based scheme. The proposed mathematical analysis studies the relationship between the average number of hops and other ad hoc network parameters such as server distance, node density, and access pattern.at last Mathematical results shows that the average number of hops is analytically predictable for the zone based scheme. Keywords: Average number of hops, cooperation zone and zone based scheme. 1 Introduction Due to the unpredictable mobility behaviors of nodes, rapid changes in the MANET topology are presumable. These changes can result in partitioning, dividing the network into isolated sub-networks. Thus, data availability in MANETs is lower than fixed networks. A solution to the problem of low data availability (accessibility) in mobile networks is cooperative caching techniques [1][2][3][4]. Cooperative Caching for MANETs is the coordination of several nodes to share a cached data in an efficient way for all. Cachingbased systems let nodes to cache data or path to a data item in order to increase data availability and achieve lower query delay times. Caching reduces communication cost of the system, which consequently results in the reduction of bandwidth and energy usage. Note

2 45 that, caching techniques are developed as middleware layer between a routing protocol and an application layer protocol. It helps the lower layer communication protocols for finding an efficient route to the owners of the data item, which is requested by an application layer protocol. It gives a caching decision based on the data on-the-fly. In general, caching approaches try to achieve that neighboring nodes cache different data items. This will increase the data diversity in a region and as a result, more data will be accessible in the network [1]. The existing work improve data availability and access efficiency by collaborating local resources of mobile nodes [4] focuses mainly on average response delay and average energy cost in the mathematical analysis. However, the existing research derives the average number of hops based on simulations, lacking the mathematical analysis of this essential metric. This paper presents a mathematical study of the number of hops in the cooperation zone based on zone based scheme. The rest of the paper is organized as follows. Section 2 reviews the related work. Section 3 presents mathematical analysis of the average number of hops. Section 4 presents detailed computational results. Section 5 concludes the paper. 2 Related Work A cooperative caching scheme specifies how multiple computing nodes share and manage the cached data in a collaborative manner [4]. Lim [5] presents a simple cooperative caching scheme for Internet based Mobile Ad Hoc Networks (IMANET) is proposed in [9]. An IMANET consists a set of mobile terminals connected with each other to form an Ad Hoc network. Among the mobile terminals, some of them can directly connected to the internet and thus serve as access points (APs) for the rest of the mobile terminals. If a mobile terminal is located far away from an AP, it can access the internet through multi-hop communication. User requests are forwarded to the AP hop by hop. If one of the mobile terminals located along the path has the requested data item in its cache, it can serve the request without forwarding it to the AP, which will improve information access efficiency. A broadcast based Simple Search (SS) algorithm and an aggregate caching mechanism are proposed for improving the information accessibility and reducing average communication latency in IMANET. As part of the aggregate cache, a cache admission control policy and a cache

3 46 replacement policy, called Time and Distance Sensitive (TDS) replacement, are developed to reduce the cache miss ratio and improve the information accessibility. They evaluate the impact of caching, cache management, and access points, which are connected to the Internet, through extensive simulation. The simulation results indicate that the proposed aggregate cache can significantly improve an IMANET performance in terms of throughput and average number of hops to access data. Shen et al [6] presented an adaptive cooperative caching scheme for mobile ad hoc networks. In their scheme, they define a zone of a mobile terminal as a set of neighbor mobile terminals within a number of hops. If a request is generated, it is first broadcasted to the mobile terminals in the zone to retrieve the requested data item. If the data is not cached in the zone, a peer-to-peer communication is used to forward request to the base station. By limiting the broadcast range to a fixed number of hops, the energy and bandwidth cost of the broadcast can be reduced. They presented an adaptive scheme for mobile Web data caching, which accounts for congestion of the wireless network and energy limitation of mobile terminals. The main objective of their work is to minimize the energy cost of peer-to-peer communication among mobile terminals so as to allow for inexpensive Web access when a fixed access point is not available in the communication range of the mobile terminal. They also propose a collaborative cache management strategy among mobile terminals interacting via an ad hoc network. They further provide evaluation of the proposed solution in terms of energy consumption on mobile devices. Chanda [7] presents a scheme comprises a group of mobile clients communicating through omni-directional antennas with the same transmission range. Mobile clients access data items held as originals by other mobile clients. A mobile client that holds the original value of a data item is called data server/source/center. A data request initiated by a client is forwarded hop-by-hop along the routing path until it reaches the data source and then the data source sends back the requested data. Each mobile client maintains local cache in its hard disk. To reduce the bandwidth consumption and query latency, the number of hops between the data source/cache and the requester should be as small as possible. it is advantageous for a client to share cached data with its neighbors lying in the zone (i.e., mobile clients that are accessible in one hop). Mobile clients belonging to the zone of a given client then form a cooperative cache system for this client since the cost for communicating with them is low both in terms of energy consumption and message exchange. They presented LUV based replacement policy for ZC caching scheme in mobile ad hoc networks. The ZC scheme enables clients in a zone to share their data which helps alleviate the longer average query

4 47 latency and limited data accessibility problems in ad hoc networks. An analytical model of ZC is also developed for average number of hops and average query latency. But the performance has not being investigated. The LUV policy considers several factors such as access probability, distance between the requester and data source/cache, coherency and data size, and thus is more realistic for cooperative caching in ad hoc networks. A simulation based performance study was conducted to evaluate the ZC scheme under LUV and LRU policies. Results show that the ZC caching scheme with LUV policy performs better in terms of cache hit ratio and average query latency in comparison with LRU policy. Chan [8] depicts a set of mobile terminals that can communicate with each other and spontaneously form a dynamic network using ad hoc communication protocols. Each mobile terminal denotes a node in the MANET. Assume the communication range of a mobile terminal is R. A base station can be viewed as a data source (or data server) which can connect to the wired network. The data source contains a set of data items and can handle all the requests from the mobile terminals. If a base station is in the communication range of a mobile terminal, the terminal can contact the base station directly to access the desired data. If a mobile terminal is far away from the base station, information access must go through multi-hop communications. This also Presented a series of cooperative caching schemes (ECORP, ECORP-DP and ECORP Gready ) for energy saving. These strategies significantly reduce energy consumption and access latency in terms of average access latency and energy per query. COOP caching [4], sits on the middleware level between application and routing layer protocols. Requested items are found by using the so-called cocktail scheme cache resolution strategy. On the other hand, which data to cache is handled with a cache management strategy, which uses two simple rules to decide which data item to keep in limited cache. They categorize the items as primary and secondary based on their existence in the neighborhood. If a data item is primary, it has priority in cache. If it is secondary, then LRU (Least Recently Used) cache management algorithm is applied to the existing cache.coop also uses TTL-based consistency control mechanism for cached data items. In cache resolution, when a node receives a data request, first of all it checks its own memory. If data is not in memory, it checks its RRT (Recent Requests Table), this keeps the previously received requests. This table avoids duplicated flooding for the same data item that was previously searched. If still no matching is found, then COOP starts an adaptive flooding, which limits the number of hops that a broadcast packet can travel. Adaptive flooding aims to find a cache of the requested item in the neighborhood. If it cannot find, it directly sends the

5 48 request to the server (or source of the requested item) using the underlying routing protocol. While the request is carried to the server, if an intermediate node finds the requested item in its cache, it stops forwarding and returns the data to the requester. The performance of COOP is studied using mathematical analysis and simulations from the perspectives of data availability, time efficiency, and energy efficiency. This cooperative caching identify open optimization problem to be addressed for the size of the cooperation zone.the analysis and simulation results show that COOP significantly reduces response delay and improves data availability with proper settings of the cooperation zone radius. However, the mathematical analysis lacks the use of the metric average number of hops which is essential in predicting the number of hops in the cooperation zone. In the above mentioned techniques none has used the average number of hops to predict the number of hops in the cooperation zone. However, Chand [7] proposes the analytical model for average number of hops but the work has not been fully implemented. In this paper, we propose to use average number of hops to predict the number of hops in the cooperation zone. 3 MATHEMATICAL ANALYSIS We use the network model that was used in Yu [4] to derive the average number of hops function using binomial probability distribution. The goal of the analysis is to predict the number of hops in the cooperation zone based on zone based scheme.java program is written to compute the computational results and show the impacts of node density, server distance, and the data access pattern. 3.1 Performance metric Average number of hops: average number of hops is defined as the number of hops a request is expected to travel before it reaches the data cache/server. Reducing the hop count can reduce the query latency, bandwidth and the power consumption since fewer clients are involved in the query process [10].

6 Notations: Notations used in this analysis are listed in Table 1. Table 1 Notation Description Average number of hops of the zone based approach The radius of the cooperation zone of the requesting node The average node density The distance to the (in hops) to between the requesting node and the data server The probability that a node has a copy of data d Under a SimpleCache scheme The probability that a node in the cooperation zone of the requesting node has a copy of data d the zone based scheme The probability that a forwarding node outside Cooperation zone of the requesting node has a copy of data,under the cocktail approach The probability of getting the data at the data server 3.3 Performance analysis In this section, average number of hops function is derived for the zone based scheme and predict the number of hops for the cooperation zone based on different node density, access pattern, and server distance. To compute the number of hops for retrieving the data item by the client, the probability of data cache for each case along with number of hops is shown below: Table 2. Scenario of average number of hops scenario Probability of data cache Number of hops Local cache Cooperation zone Beyond the cooperation zone at Data server

7 50 So, is given as [ Using hop-by-hop resolution scheme [4] in the equation above, we obtain The Eq. (1) above can be explained as follows. When there is a local cache hit, the requested data item can be retrieved locally (0 hops) at Local cache with a probability probability is. In case of zone hit (1 hop distance from the requester) the access. When there is data cache Beyond the cooperation zone, the item is retrieved from a client lying on a zone (other than home zone of the requester) along the routing path at a distance of i hops from the requester with a probability (1- If none of the hops in the cooperation has cached the requested data item, it is retrieved from the data server with a probability (1-.

8 51 4 RESULTS AND DISCUSSION Average number of hops of the zone based technique for and Fig.1.Plot of average number of hops with different Fig.1. Show the relationship between the average number of hops and the cooperation zone radius r for the zone based resolution with different distance (in hops) between the requesting node and the data server, access pattern and node density The figure shows that as the distance (in hops) between the requesting node and the data server increases, the average number of hops increases until when it reaches equilibrium where it remains constant. Because no additional hop after zone based reaches equilibrium. This figure also shows the number of hops in the cooperation zone is within 4 hops.

9 52 Average number of hops for the zone based technique for. and Fig.2.Plot of average number of hops with different Fig.2.Show the relationship between the average number of hops and the cooperation zone radius for the zone based resolution with different node density distance (in hops) between the requesting node and the data server and the access pattern.the figure shows that as the node density increases, the average number of hops increases significantly until when the figure reaches the equilibrium state where it remains constant. Because no additional hop after zone based reaches equilibrium. This figure also shows the number of hops in the cooperation zone is within 4 hops.

10 53 Average number of hops for the zone based technique for and Fig.3.Plot of average number of hops with different Fig.3. Show the relationship between the average number of hops and the cooperation zone radius r for the zone based resolution with different access pattern =0.001, 0.01, 0.1, 0.5, distance (in hops) between the requesting node and the data server and node density. The Figure shows that as access pattern increases, the average number of hops increases unless when it reaches equilibrium where it remains constant. Because no additional hop after zone based reaches equilibrium. The figure also shows the number of hops in the cooperation zone is within 4 hops. 5 CONCLUSIONS In mobile ad hoc networks, the average number of hops can be used as key metric in predicting the number of hops in cooperation zone. In this paper, we presented a mathematical model for average number of hops based on zone based scheme. The

11 54 proposed analysis shows the relationship between the average numbers of hops with other ad hoc parameters such as server distance, node density, and access pattern. Finally, the results show that the average number of hops is analytically predictable for the zone based scheme REFERENCES [1] L. Yin and G. Cao, Supporting cooperative caching in ad hoc networks," Mobile Computing, IEEE Transactions on, vol. 5, no. 1, pp , [2] W. Lau, M. Kumar, and S. Venkatesh, A cache architecture in support of caching multimedia objects in MANETs," Proceedings of the 5th ACM international workshop on Wireless mobile multimedia, pp , [3] B.Tang, H.Gupta, and S.Das, Benefit based Data Caching in Ad Hoc Networks,"Network Protocols, ICNP'06. Proceedings of the th IEEE International Conference on, pp , [4] Y. Du and S. Gupta, Improving on-demand data access efficiency in MANETs with cooperative caching" Ad Hoc Networks,vol.7,pp , 2009 [5] S. Lim, W.-C. Lee, G. Cao, C.R. Das, A novel caching scheme for improving internetbased mobile ad hoc networks performance, in: ICCCN'03: IEEE International Conference on Computer Communications and Networks, Dallas, TX, USA, pp , [6] F. Sailhan, V. Issarny, Cooperative caching in ad hoc networks, in: MDM'03: Proceedings of the Fourth International Conference on Mobile Data Management, Springer- Verlag, London, UK, pp , [7] N. Chanda, R.C. Joshib and M. Misrab, Cooperative caching in mobile ad hoc networks based on data utility, Mobile Information Systems,vol.3, 19 37,2007. [8] E. Chana, W. Li and D. Chenb, Energy saving strategies for cooperative cache replacement in mobile ad hoc networks, Pervasive and Mobile Computing, vol. 5 pp.77-92, [9] L.M. Feeney, M. Nilsson, Investigating the energy consumption of a wireless network interface in an ad hoc networking environment, in: IEEE INFOCOM, [10] L. Yin and G. Cao, Supporting Cooperative Caching in Ad Hoc Networks, IEEE INFOCOM,pp