Mobility Aware Displacement Approximation Technique Based LSS and Privacy Preservation for Effective Data Routing in WSN with IoT Devices

: The Location based Service Selection (LSS) in WSN has been well studied. Towards effective LSS, an efficient Mobility Aware Displacement Approximation (MADA-LSS) based approach is presented in this article. The model monitors the mobility of mobile device and predict the possible location at different future time stamp. According to the future time stamp, the list of service locations are identified at different possible locations. According to the possible locations and service set, the method discover set of routes to reach each of the service points. For each of the route, the method estimates Data Arrival Rate (DAR), Data Claim Rate (DCR) and Data Rate Support Measure (DRSM) for both Access Point as well as route identified. Also, the method ranks the service points according to DRSM value and estimates the Trusted Handover Measure (THM) for the routes according to IoT (Internet of Things) devices of any route. By considering both THM and DSRM values an optimal service point as well as route has been selected to perform data transmission. Also, the privacy preservation is performed by using the same set of displacement approximation scheme which selects optimal encryption based on mobility parameters and time complexity and security of different encryption schemes. The proposed method improves the performance of LSS and secure routing.

The mobile users access the available services through various routes available in the WSN to complete their data access. The sensor network has number of sensor nodes to support data access and service access. The mobile nodes have the freedom to move on any direction and speed. This would change the topology and introduces number of link failure which challenges the protocol to handle the data packets and delivering to the user. Also, like any network, there are many threats can be identified which challenges the delivery of data packet to the exact legitimate node and service point.
In general, the routing in WSN is performed by discovering the routes available. It has been performed in two ways, one by broadcasting the route request throughout the network and collecting the routes available through set of reply being received. Another one is using the topology of the network. However, identified routes cannot be used for the entire transmission, as there will be set of link failure would occur due to many factors. If the sensor nodes lose their energy and would become dead this introduces link failure. Also, when the mobile node moves to a location where there is no other sensor node located within the transmission range, then there will be link failure which encourage the increased frequency of route discovery. This would affect the performance of entire network and lifetime as well.
In recent times, the IoT (Internet of Things) devices has become unavoidable part of WSN which is being used by different organizations and homes. Even though the IoT devices are not part of the network, they can be used in transmitting the data efficiently and to complete the transaction. But, the IoT devices cannot be trusted in transmitting sensitive data between the service point and users. If there exist a malicious node in the route in form of IoT devices, then they would steal the data and would involve in various threats. The selection of secure route is done by choosing a route based on the trust of nodes, behavior of nodes and so on. However, they suffer to achieve expected security performance.
The selection of secure route has great impact on the privacy of user data. The data transmission would be done over the user data which is more sensitive. If the data has been exposed, then the service trustworthy on the network as well in the market would get degrade.
However, it is necessary to preserve the user data and privacy preservation is the process of hiding sensitive user data from external world. It has been performed by enforcing different approaches of access restriction as well as data encryption schemes. Towards the scope, this article presents an mobility aware displacement approximation scheme which involves in the selection of secure route and enforces privacy preservation. The approach is focused on how the IoT devices can be used in transmitting the service data effectively.
On the other side, the article considered the Location based service selection (LSS) which is the most dominant entity in achieving higher QoS performance in WSN. However, the mobile node moves on various directions, it is necessary to provide the seamless service to the user. The same service would be available in various locations and to provide seamless service, it is necessary to choose the optimal service access point for the user. The selection of LSS has been performed based on various constraints like mobility, speed, direction and so on. However the method does not meet the requirements. The proposed model MADA-LSS is focused on selecting the service based on various parameters. The detailed approach is presented in the next sections.

Related Works:
The problem of LSS in WSN has been approached with several techniques. Also, the privacy preservation has been handled by various researchers. This section details set of methods related to the problem.
In [1], the author proposes a proxy source node selection mechanism by constructing the candidate region. According to the energy, a least hop route is selected to perform data transmission. A concept of user/service location information and locality-sensitive hashing (LSH) is presented towards location aware recommendation to achieve higher privacypreservation [2]. A dual privacy preserving (DPP) scheme is presented to protect the user location where the method combine Shamir threshold mechanism, dynamic pseudonym mechanism, and K-anonymity technology in improving the content privacy.
In [4], a novel privacy-preserving and scalable service recommendation scheme is presented which works based on SimHash, named SecRec SimHash. In [5], the same is enforced with distributed recommendation of services, which consider multi dimensions named SerRec multi-qos , Similarly, an instance of Locality-Sensitive Hashing (LSH), into service recommendation is presented named MinHash which works on two stage as SerRec two-LSH and generates recommendation in two stages [6].
The privacy preservation with edge computing is presented in [7], where the ECO method enforce privacy preservation on vehicle to vehicle communication and V2V routing [7] A random walk approach is presented in [8], which combines collaborative filtering to perform privacy preservation and location strategy to perform LSS. A distributed locality sensitive hashing scheme based service recommendation is presented in [9], where Distri-LSH approach focused on service recommendation. In [10], propose a location privacy protection method to satisfy users' personalized privacy needs with reasonable protection of their privacy. Firstly, we define a normalized decision matrix to describe the efficiency and privacy effects of a route, and establish a multi-attribute utility function to quantify the utility of different routes for route selection. Then, according to users' personalized privacy protection need, we allocate the privacy budget for each query location on the selected route based on the distance between it and his nearest sensitive location.
In [11], they propose a Skyline point which divides the entire service set into regions.
According to that, the method performs service selection. In [12], propose noise-added selection algorithm, a location privacy protection method that satisfies differential privacy to prevent the data from privacy disclosure. In [13], propose a cooperative edge caching scheme, which allows vehicles to fetch one content from multiple caching servers cooperatively. In [14], the author propose dedicated short-range communication (DSRC) standard, with three original contributions. The method works on probability and uses the spatial relationship to perform location based service selection. In [15], develop a privacy preserving protocol to predict missing QoS values and thereby providing Web service recommendations based on past QoS experiences and locations of users. In [16], propose a privacy preserving user-based CF technique based on homomorphic encryption, which is capable of determining similarities among users followed by generating recommendations without revealing any private information.
In [17], propose a location-aware personalized CF method for Web service recommendation. The proposed method leverages both locations of users and Web services when selecting similar neighbors for the target user or service. In [18], propose a privacy-preserving task recommendation scheme (PPTR) for crowdsourcing, which achieves the task-worker matching while preserving both task privacy and worker privacy.
In [19], A customer-aware power regulatory model is proposed that provides awareness to the consumer regarding the usage of electrical energy, in a secure and reliable solution that combines the features of electrical engineering with cloud computing to ensure better performance in notifying issues, which is done based on location, and enhances the execution of smart grids.
In [20], the author present a efficient approach towards privacy preservation is presented by considering different parameters of mobile node in secure routing. In [21], proposed an energy-aware QoS-guaranteed workflow management mechanism. In [22], propose a secure and efficient privacy-preserving data aggregation algorithm (SECPDA) based on the original clustering privacy data aggregation algorithm. In [23], an efficient mobility aware directional service fetching algorithm (MADSF) is presented in this paper. The algorithm performs lookup about different services available around the user location. By fetching the trust are relation on verified route selection represents the directional approach based on the services identified, services present in the direction of user and estimates data pickup rate (DPR) for each service based on different properties of the route and service. Finally, user selected the current meet by the point single service selection and produces result to the user.
In [24], propose a proxy source node selection mechanism by constructing the candidate region. Secondly, based on the residual energy of the node, we propose a shortest routing algorithm to achieve better forwarding efficiency. Finally, by combining the proposed proxy source node selection mechanism with the proposed shortest routing algorithm based on the residual energy, we further propose a new, anonymous communication scheme.
All the methods suffer to achive the expected performance in LSS and privacy preservation.

Mobility Aware Displacement Approximation Based LSS and Privacy Preservation:
The  The above discussed algorithm computes the mobility displacement would occur on any mobile node and based on that set of service points and routes to reach the service points are identified. Identified routes and access points are added to concern set to support location based service selection.

DRSM Estimation:
The routes identified to reach any service access point has many features in terms of number of hops, number of IoT devices, traffic in each hop, number of bytes claimed per second and so on. According to all these, the data rate support measure (DSRM) is measured. The above mentioned factors are much essential in identifying the exact route for the data transmission. In order to provide seamless service, the data rate in the route towards the specific application must be achieved. For example, when a user access the video file through the service, the playback of video must be done without interruption and requires seamless delivery of data packets. This is in turn needs specific data rate required by the application must be maintained. Also, the service point must be capable of delivering such required data rate according to the user needs as well as service requirements. By maintaining such data rates, the service performance and application performance can be maintained. So, the selection of route must be done by considering all these.
According to this, for any given route R, the method estimates the Data Arrival Rate (DAR) and data Data Claim Rate (DCR) for the route. According to these two values, the Data Rate Support Measure (DRSM) of the route has been measured. Similarly, the same DRSM value can be measured for Access Point towards LSS.
The value of DAR is measured as follows: The above equation represent the value of DAR which is computed by summing the total bytes transferred through the route at different transmission and computes the average value.
Similarly, the data claim rate (DCR) is measured by computing the average claim value in bytes. It has been measured as follows: Now using DAR and DCR values, the value of DRSM is measured is measured as follows: Estimated value of DRSM is used in LSS and privacy preservation.

THM Estimation:
The trusted handover measure (THM) is the value which represents the trustworthy of the For each route R Measure THM.

End
Route R =Choose route with maximum THM.
Perform Privacy Preservation on data D.
Transmit data through route R.

Stop
The above discussed algorithm represents how the overall mobility aware displacement approximation based LSS is working.

Privacy Preservation:
The privacy preservation in this approach is performed by choosing an effective data encryption scheme according to the data. The method classifies the encryption schemes according to the complexity of the algorithm and time complexity. By considering the mobility of the node, the method selects a optimal method for data encryption. Further, the selected method has been used in data encryption and data has been transmitted through the route selected. If the node moves on less speed, then the method choose a moderate time complex algorithm for data encryption, otherwise if the nodes mobility is higher, then the least time complex scheme is selected for data encryption. But it is not done intentionally, because the selection at the least mobility is done on random manner but when mobility is higher then only least time complex method is selected.

Results and Discussion
The proposed mobility aware displacement approximation based location based service selection is implemented in advanced java. The method tracks the nodes mobility by fetching the GPS location and based on the mobility speed the service selection has been optimized. The method has been evaluated for its performance under various parameters. Obtained results are compared in this section.

Simulation Constraint Value
Tool Used Advanced Java

Number of Nodes 200
Number of service points 20 Number of IoT devices 40  The performance of the methods are measured on location based service selection and compared with other methods. The proposed MADA-LSS algorithm has improved the performance in location based service selection than other methods.

Figure 2: Analysis on LSS Performance
The performance of LSS has been measured for various approaches and presented in Figure 2. The proposed MADA-LSS algorithm has produced higher performance than other methods.      The performance of secure routing has been measured for various approaches and presented in Figure 4. The proposed MADA-LSS algorithm has produced higher performance than other methods.