Azlan Awang

A Cross-Layer Opportunistic MAC/Routing protocol to improve reliability in WBAN

Wireless Body Area Network (WBAN) is a modern technology having a wide-range of applications. Reliability is a key performance index for WBANs, especially for medical applications. An unreliable WBAN may severely affect the patient under observation. Main causes of unreliability are inefficient routing, low transmission range, body shadowing and environmental interference. We propose a Cross-Layer Opportunistic MAC/Routing (COMR) protocol that can improve the reliability by using a timer based approach for the relay selection mechanism. The value of this timer depends on Received Signal Strength Indicator (RSSI) and residual energy. The selected relay node will have the highest residual energy and will be closest to the sink as compared to other possible relay nodes. We evaluate the performance of the proposed mechanism in terms of network lifetime, Packet Delivery Ratio (PDR), End-To-End (ETE) delay, and energy used per bit. Results depict that COMR protocol shows improvement in terms of reliability, energy efficiency, ETE delay and network lifetime in a WBAN as compared to Simple Opportunistic Routing (SOR).

Data Aggregation Using Dynamic Selection of Aggregation Points Based on RSSI for Wireless Sensor Networks

In wireless sensor networks (WSNs), due to dense deployment, sensory data gathered by sensor nodes in close proximity tend to exhibit high correlation and therefore redundant. Transmitting such redundant data is not practical in the energy-constrained WSNs. Data aggregation offers a key solution to reduce such redundancy by allowing intermediate nodes to aggregate raw data streams before routing them toward a sink node. This in turn reduces transmission energy consumption. Prior work in data aggregation often rely on node’s location for selecting an aggregator node, a fusion point. In this work, we propose two data aggregation mechanisms where aggregator nodes are determined opportunistically without dependency on global knowledge of data flow, network topology and nodes’ geographical location. These mechanisms aggregate and route data packets based on Received Signal Strength Indicator (RSSI). An aggregation identification (Agg_ID) is associated with each data packet generated by a sensor node. The RSSI and Agg_ID are used in the RSSI-Based Fowarding for favoring nodes closer to sink to be an aggregator and also a relay node. We show via simulation the performance of the proposed mechanisms in terms of normalized number of transmissions, total number of packets transmissions and receptions, average energy consumed per data packet, network lifetime, end-to-end delay and packet loss probability.

A congestion-aware and energy efficient traffic Load balancing Scheme for routing in WSNs

Most of the on-demand routing protocols in WSNs are designed with the aim to find the best path based on a single routing metric such as minimum hop count, so that the End-to-End (ETE) delay can be minimized. In these protocols, the probability to select the same sensor node(s) all the time is high, resulting in overloaded nodes and increased energy consumption per node. Furthermore, in these protocols, each intermediate node acts only on the first request it receives and the subsequent requests are ignored during route discovery, leading to suboptimal routes. In this paper, the Congestion-aware energy efficient and traffic Load balancing Scheme (CLS) for routing in WSNs is proposed. This scheme utilizes the ignored information during the route discovery process and considers a composite metric that incorporates the consumed energy E, participation level P of the node and signal strength S of the link between the nodes. In addition, through extensive simulations using ns-2.35, we demonstrate the effect of weighted additive approach against the lexical approach for multi variable routing metric.

Distributed on-demand multi-optional routing protocol in multi-hop wireless networks

In Wireless Sensor Networks (WSNs), fair utilisation of the network resources is a challenging task. In this regard, one of the determining factors is the routing protocol that plays an important role to route the data traffic from the source to the destination, under limited network resources. Most of the routing protocols and their variants in WSNs replace the routing metric of the prevalent routing protocol with their proposed routing metric and keep the route discovery mechanism intact. However, replacing only the routing metric is insufficient to improve the performance of the entire network as it can generate high End-to-End (ETE) delay in finding the destination as per the routing criteria. In this paper, a novel single path loop-free Distributed On-demand Multi-Optional routing (DOMO) protocol is presented. The DOMO selects the best sensor node based on the routing metric(s) amonst various alternatives at each stage during forward route formation. A simulation has been performed in ns-2.35 and it has been observed that, using the same routing metric(s), DOMO performs better for long-term communication in WSNs as compared to Ad hoc On Demand Distance Vector (AODV). The results have revealed that DOMO improves the average ETE delay by 17% and normalises the routing load by 47% as compared to AODV.

Congestion-aware and traffic load balancing scheme for routing in WSNs

Congestion in a Wireless Sensor Network (WSN) is one of the causes of performance degradation due to severe packet loss that leads to excessive energy consumption. Solutions in WSNs try to avoid and overcome congestion by selecting sensor nodes with sufficient buffer space and adjusting the traffic rate at the source node over the shortest discovered route that usually decreases the End-to-End (ETE) throughput. On-demand routing protocols have the potential to discover the least congested route when it is required. In a WSN, most of the on-demand routing protocols replace the routing metric of the prevalent routing protocol with their proposed routing metric and keep the route discovery mechanism intact, which is not sufficient to increase the performance of the WSN. To address these problems, a novel Congestion-aware and Traffic Load balancing Scheme (CTLS) for routing has been proposed. The CTLS proactively avoids congestion through a novel route discovery mechanism to select the optimum node based on a composite metric. If congestion occurs, CTLS tries to detect it in a timely manner and alleviates it reactively using a novel ripple-based search approach. The simulation results show that the CTLS performs better as compared to the congestion avoidance, detection and alleviation and no congestion control schemes in terms of packet delivery ratio, ETE delay, throughput, and energy consumption per data packet in a resource constraint wireless network.

Hybrid MAC Protocols in VANET: A Survey

Various ongoing research efforts in Vehicular Ad hoc Network (VANET) related to safety and non-safety applications have been published in academia and industry. One of the main areas in VANET that is still lacking of significant research contributions is toward designing reliable Medium Access Control (MAC) protocols. Existing surveys on MAC protocols in VANET mostly discuss general MAC methods, that include contention-based and contention-free MAC protocols. A hybrid MAC protocol that adopts both contention-based and contention-free MAC, proposed to enhance the network performance in VANET, is fruitful to be explored further. Hence, a survey of hybrid MAC protocols for VANET is presented in this paper. The benefits and limitations of the existing hybrid MAC protocols are discussed based on their classification to provide some insights into the recent advancement of high-performance MAC protocols for vehicular networks. Finally, some open research issues are highlighted as part of future research directions that need further investigations.

A Receiver-Based Forwarding Scheme to Minimize Multipath Formation in VANET

Receiver-based data forwarding schemes are well suited for vehicular environment due to their ability of making routing decision on the fly. However, existing receiver-based schemes still face the challenges of unwanted multiple paths formation especially when contending nodes are out of transmission range of each other. In this paper, we propose an approach of the receiver-based forwarding scheme where receiving nodes decide whether to participate in contention for forwarding right based on signal-to-interference-plus-noise ratio (SINR) and forwarding zone. Upon qualifying to contend for forwarding right, the contending nodes set their waiting time based on geographical progress toward destination. We present the proposed scheme and then highlight some possible issues that require further investigation. The proposed scheme tends to minimize unnecessary formation of multiple paths toward the destination while also favors the selection of a forwarding node closer to destination.

Receiver-based data forwarding in vehicular ad hoc networks

Efficient Routing in Vehicular Ad Hoc Networks (VANETs) is one of the main challenges faced while providing reliable communication in the vehicular environment. Routing approaches that make routing decisions on the fly during data transmissions are much more suitable for the high mobility and variable density conditions present in the vehicular environment. Receiver-based data forwarding is one such routing approach where the receiving nodes decide whether or not to forward the data packet. However, existing work on receiver based schemes in VANET highlight two issues. Firstly, the unwanted multiple path formation when the receiving nodes are not in transmission range of each other. Secondly, most of the existing receiver-based data forwarding schemes make use of forwarding zone concept which limits the potential contenders thereby reducing the chances of optimal path selection. In this paper, we propose the idea of a receiver-based data forwarding scheme that addresses the above mentioned issues. The proposed scheme avoids multiple path formation by making use of feedback approach. Also, the receiving node decides whether or not to contend for the forwarding right based signal to interference plus noise ratio (SINR) and geographic progress rather than using the forwarding zone concept. After briefly discussing the proposed scheme, we then highlight the possible issues that require further investigation.

Performance evaluation of cross-layer opportunistic MAC/routing with node's mobility for wireless body area networks

Wireless Body Area Networks (WBANs) offer many new promising applications in the area of remote health monitoring. In WBANs, sensor nodes deployed on a human body often exhibit high mobility patterns due to body posture movement. In a prior work, a Cross-layer Opportunistic MAC/Routing (COMR) protocol has been proposed for multi-hop WBAN but the evaluation was made using a static network topology. As an extension, in this work the performance of COMR protocol is investigated taking into account nodes mobility with different speeds depending on body positions. The impacts of varying payload sizes are evaluated for both COMR and Simple Opportunistic Routing (SOR) protocols using a mobility model in two scenarios: standing and walking. Simulation results exhibit that COMR performs better than SOR in terms of reliability, network lifetime, end-to-end (ETE) delay and energy efficiency.

Architecture for Latency Reduction in Healthcare Internet-of-Things Using Reinforcement Learning and Fuzzy Based Fog Computing

Internet-of-Things (IoT) generate large data that is processed, analysed and filtered by cloud data centres. IoT is getting tremendously popular: the number of IoT devices worldwide is expected to reach 50.1 billion by 2020 and from this, 30.7% of IoT devices will be made available in Healthcare. Transmission and analysis of this much amount of data will increase the response time of cloud computing. The increase in response time will lead to high service latency to the end-users. The main requirement of IoT is to have low latency to transfer the data in real-time. Cloud cannot fulfill the QoS requirement in a satisfactory manner. Both the volume of data as well as factors related to internet connectivity may lead to high network latency in analyzing and acting upon the data. The propose research work introduces a hybrid approach that combines fuzzy and reinforcement learning to improve service and network latency in healthcare IoT and cloud. This hybrid approach integrates healthcare IoT devices with the cloud and uses fog services with Fuzzy Reinforcement Learning Data Packet Allocation (FRLDPA) algorithm. The propose algorithm performs batch workloads on IoT data to minimize latency and manages the QoS of the latency-critical workloads. It has the potential to automate the reasoning and decision making capability in fog computing nodes.