A Reliable Framework for Secure Communication and Health Assessment of Soldiers in the Battlefield

Abstract

Wars against the enemy are mandatory for the integrity of a nation. With the passage of time, the nature of war is being changed, to some extent from a conventional war to the network-centric warfare. The rate of soldiers death has increased in modern combat operations. The lack of secure connectivity and the health (mental and physical) of soldiers are among a few major reasons for the increased mortality. Mental health or stress level produces permanent changes in the physiological systems of a soldier and long-term diseases such as asthma, diabetes, and hypertension are activated with chronic stress. Although modern communication technologies may provide huge support in the efficient conduction of combat’s activities but it is at the cost of huge power consumption. The proposed research presents a computationally efficient and low-power framework that not only ensures secure communication but also provides a robust command and control system to monitor the mental stress, injury, and death of soldiers. In the suggested framework, the Advanced Encryption Standard (AES) is used for communication that is highly suitable for power constraint devices. A wearable sensor system is suggested that can be embedded in a soldier’s uniform to assist in the recording of real-time stress, injury, and death of soldiers. The system contains a heart-rate monitor to record variations in heartbeats, an EDA sensor for skin conductance, and a respiration sensor for respiration variations along with a holster unit for storage and transmission of biomedical signals. Furthermore, it also provides a mechanism to prevent misuse of soldiers’ communication equipment by the enemy thus ensuring more security. The experimental analysis shows that the proposed mental stress computation module achieved 83.33% accuracy. Moreover, a score-based comparison is performed with the existing techniques and it is found that the proposed mechanism outperformed its counterparts with 37.5% improvements in the features-based analysis. The flexibility, security, efficiency, and reliability of the framework make it appropriate for modern warfare scenarios.