Reliability of Mobile Agents for Reliable Service Discovery Protocol in MANET

Recently mobile agents are used to discover services in mobile ad-hoc network (MANET) where agents travel through the network, collecting and sometimes spreading the dynamically changing service information. But it is important to investigate how reliable the agents are for this application as the dependability issues(reliability and availability) of MANET are highly affected by its dynamic nature.The complexity of underlying MANET makes it hard to obtain the route reliability of the mobile agent systems (MAS); instead we estimate it using Monte Carlo simulation. Thus an algorithm for estimating the task route reliability of MAS (deployed for discovering services) is proposed, that takes into account the effect of node mobility in MANET. That mobility pattern of the nodes affects the MAS performance is also shown by considering different mobility models. Multipath propagation effect of radio signal is considered to decide link existence. Transient link errors are also considered. Finally we propose a metric to calculate the reliability of service discovery protocol and see how MAS performance affects the protocol reliability. The experimental results show the robustness of the proposed algorithm. Here the optimum value of network bandwidth (needed to support the agents) is calculated for our application. However the reliability of MAS is highly dependent on link failure probability

IRT-SD-SLE:an improved real-time step detection and step length estimation using smartphone accelerometer

Smartphone sensor-based pedestrian dead reckoning (PDR) systems provide a viable solution to the problem of localization in an infrastructure-less area. Step detection (SD) and step length estimation (SLE), being two fundamental operations of the PDR-based localization technique, have drawn many researchers’ attention in the recent time. Most of the existing SD and SLE methods proposed over the years, however, provide either server-or cloud-based solution that consume additional network bandwidth and suffer from increased transmission delay. Moreover, nonavailability of the inertial sensors like gyroscope, magnetometers, etc., at every smartphone makes majority of the existing SLE methods less applicable to such devices. To address the above-said issues, in this article, we focus on devising an improved SLE method that would detect the pedestrian’s steps and subsequently estimate the step length in real-time by processing the accelerometer data at the device itself. Our proposed method transforms the measured acceleration values along the Earth coordinate system (ECS) and also applies sliding window meaning (SWM) to mitigate the negative effects of the smartphone’s orientation and gravitational bias on the accuracy of SD and SLE. The performances of our proposed method are evaluated in terms of accuracy for ten different users by taking the device in two different postures (handheld and trouser pocket) under two different walking modes (normal and fast) to demonstrate its efficacy. Moreover, our proposed method obtains more than 80% average accuracy for SD and also obtains more than 75% accuracy (median) for SLE for all participants under four different scenarios considered here

Designing Transmission Strategies for Enhancing Communications in Medical IoT Using Markov Decision Process

The introduction of medical Internet of Things (IoT) for biomedical applications has brought about the era of proactive healthcare. Such advanced medical supervision lies on the foundation of a network of energy-constrained wearable or implantable sensors (or things). These miniaturized battery-powered biosensor nodes are placed in, on, or around the human body to measure vital signals to be reported to the sink. This network configuration deployed on a human body is known as the Wireless Body Area Network (WBAN). Strategies are required to restrict energy expenditure of the nodes without degrading performance of WBAN to make medical IoT a green (energy-efficient) and effective paradigm. Direct communication from a node to sink in WBAN may often lead to rapid energy depletion of nodes as well as growing thermal effects on the human body. Hence, multi-hop communication from sources to sink in WBAN is often preferred instead of direct communication with high transmission power. Existing research focuses on designing multi-hop protocols addressing the issues in WBAN routing. However, the ideal conditions for multi-hop routing in preference to single-hop direct delivery is rarely investigated. Accordingly, in this paper an optimal transmission policy for WBAN is developed using Markov Decision Process (MDP) subject to various input conditions such as battery level, event occurrence, packet transmission rate and link quality. Thereafter, a multi-hop routing protocol is designed where routing decisions are made following a pre-computed strategy. The algorithm is simulated, and performance is compared with existing multi-hop protocol for WBAN to demonstrate the viability of the proposed scheme

An Ensemble of Condition Based Classifiers for Device Independent Detailed Human Activity Recognition Using Smartphones

Human activity recognition is increasingly used for medical, surveillance and entertainment applications. For better monitoring, these applications require identification of detailed activity like sitting on chair/floor, brisk/slow walking, running, etc. This paper proposes a ubiquitous solution to detailed activity recognition through the use of smartphone sensors. Use of smartphones for activity recognition poses challenges such as device independence and various usage behavior in terms of where the smartphone is kept. Only a few works address one or more of these challenges. Consequently, in this paper, we present a detailed activity recognition framework for identifying both static and dynamic activities addressing the above-mentioned challenges. The framework supports cases where (i) dataset contains data from accelerometer; and the (ii) dataset contains data from both accelerometer and gyroscope sensor of smartphones. The framework forms an ensemble of the condition based classifiers to address the variance due to different hardware configuration and usage behavior in terms of where the smartphone is kept (right pants pocket, shirt pockets or right hand). The framework is implemented and tested on real data set collected from 10 users with five different device configurations. It is observed that, with our proposed approach, 94% recognition accuracy can be achieved

Designing an energy efficient WBAN routing protocol

Advancement of medical science brings together new trends of proactive health care which gives rise to the era of Wireless Body Area Networks (WBAN). A number of issues including energy efficiency, reliability, optimal use of network bandwidth need to be considered for designing any multi-hop communication protocol for WBANs. Energy consumption depends on many factors like amount and frequency of forwarding traffic, node activity, distance from sink etc. Energy consumption gives rise to other issues like heated nodes. Existing routing protocols are mostly single hop or multi-hop, and generally focus on one issue ignoring the others. In this paper, we first identify the sources of energy drain, and then propose a 2-hop cost based energy efficient routing protocol for WBAN that formulates the energy drain of a node due to various reasons and incorporates it in the routing decision. Relative node mobility due to posture change is also considered here. The protocol is simulated in Castalia simulator and compared with state of the art protocols. It is found to outperform state of the art protocols in terms of packet delivery ratio for a given transmission power level. Moreover, only a small number of relays are found to be sufficient to stabilize packet delivery ratio

Secure lightweight routing (SLR) strategy for wireless body area networks

Wireless Body Area Networks (WBANs) are becoming a popular choice for a wide range of monitoring applications such as healthcare, sport activity and rehabilitation systems. However, lack of security in WBANs may hamper the wide public acceptance of this technology. Open nature of the wireless medium, makes the patients' data prone to eavesdropping, modification, or loss. Few existing WBAN routing protocols can be found on providing secure authentication using encryption mechanisms, however they do not consider lightweight communication approach. In this paper we propose an energy efficient mechanism that prevents malicious intruders from dropping data packets or forwarding fake data. The mechanism is applied to Adhoc On-demand Distance Vector (AODV) protocol though it can work with any other reactive WBAN routing protocol. The protocol is simulated and results show its effectiveness in detecting malicious nodes with low overhead

Two Phased Routing Protocol Incorporating Distributed Genetic Algorithm and Gradient Based Heuristic in Clustered WSN

In wireless cluster networks with a single non mobile sink, finding the optimal cluster assignment is a non-trivial problem. The inherently non centralized nature of wireless sensor networks poses a problem as majority of the learning algorithms are centralized. It is also desirable that single routing algorithm be applicable regardless of whether the sensor network is a dense single-hop network or a sparse multi-hop network. In this paper we present the two phased routing incorporating distributed genetic algorithm and gradient based heuristic (TRIGGER) as an attempt to solve these problems. In the first phase of TRIGGER a distributed (island model) genetic algorithm based clustering is employed to find a spatially optimal cluster assignment. In the second phase a gradient based routing forwards the already aggregated data to the sink. We discuss the rationale behind the two phased nature of TRIGGER. We demonstrate the effectiveness of TRIGGER with extensive simulations and discuss the results

Reinforcement learning based effective communication strategies for energy harvested WBAN

This paper proposes effective communication strategies for Wireless Body Area Networks (WBANs) that consist of wearable or implantable sensor nodes placed in, on/around the human body to send body vitals to a sink. The main research challenges for communication strategy formulation include limited energy resources and varying link conditions. Though energy harvested sensor nodes partially address the problem of energy efficiency, finding an optimal balance between the energy constraint of the nodes and communication reliability is still challenging. Since data loss in such networks may prove to be fatal, it is important to investigate the problem prior to deployment and come up with effective communication strategies for initiating post-deployment operations. Hence, in this paper, the nodes are stochastically modeled as a Markov Decision Process. There is a need to adapt to the changing ambient conditions through exploration and exploitation. So, a modified Q-learning technique is proposed for post-deployment decision-making by the WBAN nodes subject to the dynamic ambient conditions. The effectiveness of the proposed strategy is validated through extensive simulation and compared with state-of-the-art works. The performance of the proposed approach is also verified with a real-life dataset. The results demonstrate that around 90% successful data delivery to sink could be made with the proposed scheme in the real-life scenario