Health monitoring using biomedical sensors has witnessed significant attention in recent past due to the evolution of a new research area in sensor network known as Wireless Body Area Networks (WBANs). In WBANs, a number of implantable, wearable, and off-body biomedical sensors are utilized to monitor various vital signs of patient’s body for early detection, and medication of grave diseases. In literature, a number of Medium Access Control (MAC) protocols for WBANs have been suggested for addressing the unique challenges related to reliability, delay, collision and energy in the new research area. The design of MAC protocols is based on multiple access techniques. Understanding the basis of MAC protocol designs for identifying their design objectives in broader perspective, is a quite challenging task. In this context, this paper qualitatively reviews MAC protocols for WBANs. Firstly, 802.15.4 and 802.15.6 based MAC Superframe structures are investigated focusing on design objectives. Secondly, different multiple access techniques such as TDMA, CSMA/CA, Slotted Aloha and Hybrid are explored in terms of design goals. Thirdly, a two-layered taxonomy is presented for MAC protocols. First layer classification is based on multiple access techniques, whereas second layer classification is based on design objectives and characteristics of MAC protocols. Critical and qualitative analysis is carried out for each considered MAC protocol. Comparative study of different MAC protocols is also performed. Finally, some open research challenges in the area are identified with initial research directions.

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Recently, Wireless Body Area Network (WBAN) has witnessed significant attentions in research and product development due to the growing number of sensor-based applications in healthcare domain. Design of efficient and effective Medium Access Control (MAC) protocol is one of the fundamental research themes in WBAN. Static on-demand slot allocation to patient data is the main approach adopted in the design of MAC protocol in literature, without considering the type of patient data specifically the level of severity on patient data. This leads to the degradation of the performance of MAC protocols considering effectiveness and traffic adjustability in realistic medical environments. In this context, this paper proposes a Traffic Priority-Aware MAC (TraPy-MAC) protocol for WBAN. It classifies patient data into emergency and non-emergency categories based on the severity of patient data. The threshold value aided classification considers a number of parameters including type of sensor, body placement location, and data transmission time for allocating dedicated slots patient data. Emergency data are not required to carry out contention and slots are allocated by giving the due importance to threshold value of vital sign data. The contention for slots is made efficient in case of non-emergency data considering threshold value in slot allocation. Moreover, the slot allocation to emergency and non-emergency data are performed parallel resulting in performance gain in channel assignment. Two algorithms namely, Detection of Severity on Vital Sign data (DSVS), and ETS Slots allocation based on the Severity on Vital Sign (ETS-SVS) are developed for calculating threshold value and resolving the conflicts of channel assignment, respectively. Simulations are performed in ns2 and results are compared with the state-of-the-art MAC techniques. Analysis of results attests the benefit of TraPy-MAC in comparison with the state-of-the-art MAC in channel assignment in realistic medical environments.

Wireless body area network (WBAN) has witnessed significant attentions in the healthcare domain using biomedical sensor-based monitoring of heterogeneous nature of vital signs of a patient's body. The design of frequency band, MAC superframe structure, and slots allocation to the heterogeneous nature of the patient's packets have become the challenging problems in WBAN due to the diverse QoS requirements. In this context, this paper proposes an Energy Efficient Traffic Prioritization for Medium Access Control (EETP-MAC) protocol, which provides sufficient slots with higher bandwidth and guard bands to avoid channels interference causing longer delay. Specifically, the design of EETP-MAC is broadly divided in to four folds. Firstly, patient data traffic prioritization is presented with broad categorization including Non-Constrained Data (NCD), Delay-Constrained Data (DCD), Reliability-Constrained Data (RCD) and Critical Data (CD). Secondly, a modified superframe structure design is proposed for effectively handling the traffic prioritization. Thirdly, threshold based slot allocation technique is developed to reduce contention by effectively quantifying criticality on patient data. Forth, an energy efficient frame design is presented focusing on beacon interval, superframe duration, and packet size and inactive period. Simulations are performed to comparatively evaluate the performance of the proposed EETP-MAC with the state-of-the-art MAC protocols. The comparative evaluation attests the benefit of EETP-MAC in terms of efficient slot allocation resulting in lower delay and energy consumption.

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Agricultural food production is projected to be 70% higher by 2050 than it is today, with the world population rising to more than 9 billion, 34% higher than it is now. The farmers have been forced to produce more with the same resources. This pressure means that optimizing productivity is one of the main objectives of the producers but also in a sustainable way. Not only does agriculture face a decline in production, but it has also had to face limitations in data collection, storing, securing, and sharing, climate change, increases in input prices, traditional food supply chain systems where there is no direct connection between the farmer and the buyer, and limitations on energy use. Existing IoT-based agriculture systems have a centralized format and operate in isolation, leaving room for unresolved issues and major concerns, including data security, manipulation, and single failure points. This paper proposes a futuristic IoT with a blockchain model to meet these challenges. Further, this paper also proposes and novel energy-efficient clustering IoT-based agriculture protocol for lower energy consumption and network stability and compares its results with its counterpart low-energy adoptive clustering hierarchy (LEACH) protocol. The simulation results show that the proposed protocol network stability is 23% higher as compared to LEACH as first node of LEACH dies at 168 rounds while IoT-based agriculture first node dies after 463 rounds. Similarly, IoT-based agriculture protocol energy consumption is 68% lower than that of LEACH. The proposed protocol also extends the network life to more rounds and demonstrates an increase of 112%.

In emergency health cases such as mass casualty incidents, the death ratio is still high due to lack of an automatic and intelligent system which timely observes and reports patient criticality. Indeed, the existing criticality assessment approaches are manual such as the established Simple Triage and Rapid Treatment (START). Accordingly, it is difficult for care givers to provide optimal healthcare, in particular, if the number of casualties outnumbers the responders. A challenge is how to automatically tag a possibly large number of victims with various types of disorders immediately after an incident and before the arrival of the paramedics. Such an automated tagging would provide for more optimized emergency response.We propose an automatic self-tagging methodology using body sensor networks that deliver relevant vital signs, i.e., respiratory rate, heart rate and mental status. We present three approaches to recognize and grade the criticality level of patients. The proposed approaches are generic and can be easily adapted to different scenario such as patients in intensive care units, patients in surgery and elderlies being monitored in their home. Being fully automated, our methodology is able to provide realtime tagging with higher accuracy and fine-granularity than the simplistic manual current systems. We demonstrate the viability of our self-tagging approaches by statistically demonstrating their accuracy compared to that of experts manual tagging.