Biblio

Intelligent service network under the paradigm of the Internet of Things (IoT) uses sensor and network communication technology to realize the interconnection of everything and real-time communication between devices. Under the background of combat, all kinds of sensor devices and equipment units need to be highly networked to realize interconnection and information sharing, which makes the Internet of Things technology hopeful to be applied in the battlefield to interconnect these entities to form the Internet of Battlefield Things (IoBT). This paper analyzes the related concepts of IoBT, and constructs the IoBT multilayer dependency network model according to the typical characteristics and topology of IoBT, then constructs the weighted super-adjacency matrix according to the coupling weights within and between different layers, and the stability model of IoBT is analyzed and derived. Finally, an example of IoBT network is given to provide a reference for analyzing the stability factors of IoBT network.

Recent concepts in defense herald an increasing degree of automation of future military systems, with an emphasis on accelerating sensing-to-decision loops at the tactical edge, reducing their network communication footprint, and improving the inference quality of intelligent components in the loop. These requirements pose resource management challenges, calling for operating-system-like constructs that optimize the use of limited computational resources at the tactical edge. This paper describes these challenges and presents IoBT-OS, an operating system for the Internet of Battlefield Things that aims to optimize decision latency, improve decision accuracy, and reduce corresponding resource demands on computational and network components. A simple case-study with initial evaluation results is shown from a target tracking application scenario.

The Internet of Battlefield Things is a relatively new cyberphysical system and even though it shares a lot of concepts from the Internet of Things and wireless ad hoc networking in general, a lot of research is required to address its scale and peculiarities. In this article we examine a fundamental problem pertaining to the routing/dissemination of information, namely the construction of a backbone. We model an IoBT ad hoc network as a multilayer network and employ the concept of domination for multilayer networks which is a complete departure from the volume of earlier works, in order to select sets of nodes that will support the routing of information. Even though there is huge literature on similar topics during the past many years, the problem in military (IoBT) networks is quite different since these wireless networks are multilayer networks and treating them as a single (flat) network or treating each layer in isolation and calculating dominating set produces submoptimal or bad solutions; thus all the past literature which deals with single layer (flat) networks is in principle inappropriate. We design a new, distributed algorithm for calculating connected dominating sets which produces dominating sets of small cardinality. We evaluate the proposed algorithm on synthetic topologies, and compare it against the only two existing competitors. The proposed algorithm establishes itself as the clear winner in all experiments.

The most widely used method to prevent adversaries from eavesdropping on sensitive sensor, robot, and war fighter communications is mathematically strong cryptographic algorithms. However, prevailing cryptographic protocol mandates are often made without consideration of resource constraints of devices in the internet of Battlefield Things (IoBT). In this article, we address the challenges of IoBT sensor data exchange in contested environments. Battlefield IoT (Internet of Things) devices need to exchange data and receive feedback from other devices such as tanks and command and control infrastructure for analysis, tracking, and real-time engagement. Since data in IoBT systems may be massive or sparse, we introduced a machine learning classifier to determine what type of data to transmit under what conditions. We compared Support Vector Machine, Bayes Point Match, Boosted Decision Trees, Decision Forests, and Decision Jungles on their abilities to recommend the optimal confidentiality preserving data and transmission path considering dynamic threats. We created a synthesized dataset that simulates platoon maneuvers and IED detection components. We found Decision Jungles to produce the most accurate results while requiring the least resources during training to produce those results. We also introduced the JointField blockchain network for joint and allied force data sharing. With our classifier, strategists, and system designers will be able to enable adaptive responses to threats while engaged in real-time field conflict.

As the unmanned aerial vehicle (UAV) can play a vital role to collect information remotely in a military battlefield, researchers have shown great interest to reveal the domain of internet of battlefield Things (IoBT). In a rescue mission on a battlefield, UAV can collect data from different regions to identify the casualty of a soldier. One of the major challenges in IoBT is to identify the soldier in a complex environment. Image processing algorithm can be helpful if proper methodology can be applied to identify the victims. However, due to the limited hardware resources of a UAV, processing task can be handover to the nearby edge computing server for offloading the task as every second is very crucial in a battlefield. Furthermore, to avoid any third-party interaction in the network and to store the data securely, blockchain can help to create a trusted network as it forms a distributed ledger among the participants. This paper proposes a UAV assisted casualty detection scheme based on image processing algorithm where data is protected using blockchain technology. Result analysis has been conducted to identify the victims on the battlefield successfully using image processing algorithm and network issues like throughput and delay has been analyzed in details using public-key cryptography.

In this paper, our objective is to focus on the recent trend of military fields where they brought Internet of Things (IoT) to have better impact on the battlefield by improving the effectiveness and this is called Internet of Battlefield Things(IoBT). Due to the requirements of high computing capability and minimum response time with minimum fault tolerance this paper proposed a decentralized IoBT architecture. The proposed method can increase the reliability in the battlefield environment by searching the reliable nodes among all the edge nodes in the environment, and by adding the fault tolerance in the edge nodes will increase the effectiveness of overall battlefield scenario. This suggested fault tolerance approach is worth for decentralized mode to handle the issue of latency requirements and maintaining the task reliability of the battlefield. Our experimental results ensure the effectiveness of the proposed approach as well as enjoy the requirements of latency-aware military field while ensuring the overall reliability of the network.

The Internet of Battlefield Things (IoBT) might be one of the most expensive cyber-physical systems of the next decade, yet much research remains to develop its fundamental enablers. A challenge that distinguishes the IoBT from its civilian counterparts is resilience to a much larger spectrum of threats.

Internet of Battlefield Things (IoBT) devices such as actuators, sensors, wearable devises, robots, drones, and autonomous vehicles, facilitate the Intelligence, Surveillance and Reconnaissance (ISR) to Command and Control and battlefield services. IoBT devices have the ability to collect operational field data, to compute on the data, and to upload its information to the network. Securing the IoBT presents additional challenges compared with traditional information technology (IT) systems. First, IoBT devices are mass produced rapidly to be low-cost commodity items without security protection in their original design. Second, IoBT devices are highly dynamic, mobile, and heterogeneous without common standards. Third, it is imperative to understand the natural world, the physical process(es) under IoBT control, and how these real-world processes can be compromised before recommending any relevant security counter measure. Moreover, unprotected IoBT devices can be used as “stepping stones” by attackers to launch more sophisticated attacks such as advanced persistent threats (APTs). As a result of these challenges, IoBT systems are the frequent targets of sophisticated cyber attack that aim to disrupt mission effectiveness.

Efficient application of Internet of Battlefield Things (IoBT) technology on the battlefield calls for innovative solutions to control and manage the deluge of heterogeneous IoBT devices. This paper presents an innovative paradigm to address heterogeneity in controlling IoBT and IoT devices, enabling multi-force cooperation in challenging battlefield scenarios.

Numerous antenna design approaches for wearable applications have been investigated in the literature. As on-body wearable communications become more ingrained in our daily activities, the necessity to investigate the impacts of these networks burgeons as a major requirement. In this study, we investigate the human electromagnetic field (EMF) exposure effect from on-body wearable devices at 2.4 GHz and 60 GHz, and compare the results to illustrate how the technology evolution to higher frequencies from wearable communications can impact our health. Our results suggest the average specific absorption rate (SAR) at 60 GHz can exceed the regulatory guidelines within a certain separation distance between a wearable device and the human skin surface. To the best of authors' knowledge, this is the first work that explicitly compares the human EMF exposure at different operating frequencies for on-body wearable communications, which provides a direct roadmap in design of wearable devices to be deployed in the Internet of Battlefield Things (IoBT).

This paper reviews the definitions and characteristics of military effects, the Internet of Battlefield Things (IoBT), and their impact on decision processes in a Multi-Domain Operating environment (MDO). The aspects of contemporary military decision-processes are illustrated and an MDO Effect Loop decision process is introduced. We examine the concept of IoBT effects and their implications in MDO. These implications suggest that when considering the concept of MDO, as a doctrine, the technological advances of IoBTs empower enhancements in decision frameworks and increase the viability of novel operational approaches and options for military effects.

In this paper, decentralized dynamic power allocation problem has been investigated for mobile ad hoc network (MANET) at tactical edge. Due to the mobility and self-organizing features in MANET and environmental uncertainties in the battlefield, many existing optimal power allocation algorithms are neither efficient nor practical. Furthermore, the continuously increasing large scale of the wireless connection population in emerging Internet of Battlefield Things (IoBT) introduces additional challenges for optimal power allocation due to the “Curse of Dimensionality”. In order to address these challenges, a novel Actor-Critic-Mass algorithm is proposed by integrating the emerging Mean Field game theory with online reinforcement learning. The proposed approach is able to not only learn the optimal power allocation for IoBT in a decentralized manner, but also effectively handle uncertainties from harsh environment at tactical edge. In the developed scheme, each agent in IoBT has three neural networks (NN), i.e., 1) Critic NN learns the optimal cost function that minimizes the Signal-to-interference-plus-noise ratio (SINR), 2) Actor NN estimates the optimal transmitter power adjustment rate, and 3) Mass NN learns the probability density function of all agents' transmitting power in IoBT. The three NNs are tuned based on the Fokker-Planck-Kolmogorov (FPK) and Hamiltonian-Jacobian-Bellman (HJB) equation given in the Mean Field game theory. An IoBT wireless network has been simulated to evaluate the effectiveness of the proposed algorithm. The results demonstrate that the actor-critic-mass algorithm can effectively approximate the probability distribution of all agents' transmission power and converge to the target SINR. Moreover, the optimal decentralized power allocation is obtained through integrated mean-field game theory with reinforcement learning.

A dynamic overlay system is presented for supporting transport service needs of dispersed computing applications for moving data and/or code between network computation points and end-users in IoT or IoBT. The Network Backhaul Layered Architecture (Nebula) system combines network discovery and QoS monitoring, dynamic path optimization, online learning, and per-hop tunnel transport protocol optimization and synthesis over paths, to carry application traffic flows transparently over overlay tunnels. An overview is provided of Nebula's overlay system, software architecture, API, and implementation in the NRL CORE network emulator. Experimental emulation results demonstrate the performance benefits that Nebula provides under challenging networking conditions.

Continued advances in IoT technology have prompted new investigation into its usage for military operations, both to augment and complement existing military sensing assets and support next-generation artificial intelligence and machine learning systems. Under the emerging Internet of Battlefield Things (IoBT) paradigm, a multitude of operational conditions (e.g., diverse asset ownership, degraded networking infrastructure, adversary activities) necessitate the development of novel security techniques, centered on establishment of trust for individual assets and supporting resilience of broader systems. To advance current IoBT efforts, a set of research directions are proposed that aim to fundamentally address the issues of trust and trustworthiness in contested battlefield environments, building on prior research in the cybersecurity domain. These research directions focus on two themes: (1) Supporting trust assessment for known/unknown IoT assets; (2) Ensuring continued trust of known IoBT assets and systems.

FastChain is a simulator built in NS-3 which simulates the networked battlefield scenario with military applications, connecting tankers, soldiers and drones to form Internet-of-Battlefield-Things (IoBT). Computing, storage and communication resources in IoBT are limited during certain situations in IoBT. Under these circumstances, these resources should be carefully combined to handle the task to accomplish the mission. FastChain simulator uses Sharding approach to provide an efficient solution to combine resources of IoBT devices by identifying the correct and the best set of IoBT devices for a given scenario. Then, the set of IoBT devices for a given scenario collaborate together for sharding enabled Blockchain technology. Interested researchers, policy makers and developers can download and use the FastChain simulator to design, develop and evaluate blockchain enabled IoBT scenarios that helps make robust and trustworthy informed decisions in mission-critical IoBT environment.

Continued advances in IoT technology have prompted new investigation into its usage for military operations, both to augment and complement existing military sensing assets and support next-generation artificial intelligence and machine learning systems. Under the emerging Internet of Battlefield Things (IoBT) paradigm, current operational conditions necessitate the development of novel security techniques, centered on establishment of trust for individual assets and supporting resilience of broader systems. To advance current IoBT efforts, a collection of prior-developed cybersecurity techniques is reviewed for applicability to conditions presented by IoBT operational environments (e.g., diverse asset ownership, degraded networking infrastructure, adversary activities) through use of supporting case study examples. The research techniques covered focus on two themes: (1) Supporting trust assessment for known/unknown IoT assets; (2) ensuring continued trust of known IoT assets and IoBT systems.

The emerging Internet of Things (IoT) applications that leverage ubiquitous connectivity and big data are facilitating the realization of smart everything initiatives. IoT-enabled infrastructures have naturally a multi-layer system architecture with an overlaid or underlaid device network and its coexisting infrastructure network. The connectivity between different components in these two heterogeneous networks plays an important role in delivering real-time information and ensuring a high-level situational awareness. However, IoT- enabled infrastructures face cyber threats due to the wireless nature of communications. Therefore, maintaining the network connectivity in the presence of adversaries is a critical task for the infrastructure network operators. In this paper, we establish a three-player three-stage game-theoretic framework including two network operators and one attacker to capture the secure design of multi- layer infrastructure networks by allocating limited resources. We use subgame perfect Nash equilibrium (SPE) to characterize the strategies of players with sequential moves. In addition, we assess the efficiency of the equilibrium network by comparing with its team optimal solution counterparts in which two network operators can coordinate. We further design a scalable algorithm to guide the construction of the equilibrium IoT-enabled infrastructure networks. Finally, we use case studies on the emerging paradigm of Internet of Battlefield Things (IoBT) to corroborate the obtained results.

Internet of Things (IoT) in military setting generally consists of a diverse range of Internet-connected devices and nodes (e.g. medical devices to wearable combat uniforms), which are a valuable target for cyber criminals, particularly state-sponsored or nation state actors. A common attack vector is the use of malware. In this paper, we present a deep learning based method to detect Internet Of Battlefield Things (IoBT) malware via the device's Operational Code (OpCode) sequence. We transmute OpCodes into a vector space and apply a deep Eigenspace learning approach to classify malicious and bening application. We also demonstrate the robustness of our proposed approach in malware detection and its sustainability against junk code insertion attacks. Lastly, we make available our malware sample on Github, which hopefully will benefit future research efforts (e.g. for evaluation of proposed malware detection approaches).

In military operations, Commander's Intent describes the desired end state and purpose of the operation, expressed in a concise and clear manner. Command by intent is a paradigm that empowers subordinate units to exercise measured initiative to meet mission goals and accept prudent risk within commander's intent. It improves agility of military operations by allowing exploitation of local opportunities without an explicit directive from the commander to do so. This paper discusses what the paradigm entails in terms of architectural decisions for data fusion systems tasked with real-time information collection to satisfy operational mission goals. In our system, information needs of decisions are expressed at a high level, and shared among relevant nodes. The selected nodes, then, jointly operate to meet mission information needs by forwarding and caching relevant data without explicit directives regarding the objects to fetch and sources to contact. A preliminary evaluation of the system is presented using a target tracking application, set in the context of a NATO-based mission scenario, called Anglova. Evaluation results show that delegating some decision authority to the data fusion system (in terms of objects to fetch and sources to contact) allows it to save more network resources, while also increasing mission success rate. The system is therefore particularly well-suited to operation in partially denied or contested environments, where resource bottlenecks caused by adversarial activity impair one's ability to collect real-time information for mission-critical decision making.

In this paper, a novel game-theoretic framework is introduced to analyze and enhance the security of adversarial Internet of Battlefield Things (IoBT) systems. In particular, a dynamic, psychological network interdiction game is formulated between a soldier and an attacker. In this game, the soldier seeks to find the optimal path to minimize the time needed to reach a destination, while maintaining a desired bit error rate (BER) performance by selectively communicating with certain IoBT devices. The attacker, on the other hand, seeks to find the optimal IoBT devices to attack, so as to maximize the BER of the soldier and hinder the soldier's progress. In this game, the soldier and attacker's first- order and second-order beliefs on each others' behavior are formulated to capture their psychological behavior. Using tools from psychological game theory, the soldier and attacker's intention to harm one another is captured in their utilities, based on their beliefs. A psychological forward induction-based solution is proposed to solve the dynamic game. This approach can find a psychological sequential equilibrium of the game, upon convergence. Simulation results show that, whenever the soldier explicitly intends to frustrate the attacker, the soldier's material payoff is increased by up to 15.6% compared to a traditional dynamic Bayesian game.

Edge computing can potentially play a crucial role in enabling user authentication and monitoring through context-aware biometrics in military/battlefield applications. For example, in Internet of Military Things (IoMT) or Internet of Battlefield Things (IoBT),an increasing number of ubiquitous sensing and computing devices worn by military personnel and embedded within military equipment (combat suit, instrumented helmets, weapon systems, etc.) are capable of acquiring a variety of static and dynamic biometrics (e.g., face, iris, periocular, fingerprints, heart-rate, gait, gestures, and facial expressions). Such devices may also be capable of collecting operational context data. These data collectively can be used to perform context-adaptive authentication in-the-wild and continuous monitoring of soldier's psychophysical condition in a dedicated edge computing architecture.

Recently, the armed forces want to bring the Internet of Things technology to improve the effectiveness of military operations in battlefield. So the Internet of Battlefield Things (IoBT) has entered our view. And due to the high processing latency and low reliability of the “combat cloud” network for IoBT in the battlefield environment, in this paper , a novel “combat cloud-fog” network architecture for IoBT is proposed. The novel architecture adds a fog computing layer which consists of edge network equipment close to the users in the “combat-cloud” network to reduce latency and enhance reliability. Meanwhile, since the computing capability of the fog equipment are weak, it is necessary to implement distributed computing in the “combat cloud-fog” architecture. Therefore, the distributed computing load balancing problem of the fog computing layer is researched. Moreover, a distributed generalized diffusion strategy is proposed to decrease latency and enhance the stability and survivability of the “combat cloud-fog” network system. The simulation result indicates that the load balancing strategy based on generalized diffusion algorithm could decrease the task response latency and support the efficient processing of battlefield information effectively, which is suitable for the “combat cloud- fog” network architecture.

The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas such as smart homes, smart cities, health care, transportation, etc. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to the battlefield specific challenges such as the absence of communication infrastructure, and the susceptibility of devices to cyber and physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and the information dissemination in the presence of adversaries. This work aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to study the communication of mission-critical data among different types of network devices and consequently design the network in a cost effective manner.

In this paper, the problem of misinformation propagation is studied for an Internet of Battlefield Things (IoBT) system in which an attacker seeks to inject false information in the IoBT nodes in order to compromise the IoBT operations. In the considered model, each IoBT node seeks to counter the misinformation attack by finding the optimal probability of accepting a given information that minimizes its cost at each time instant. The cost is expressed in terms of the quality of information received as well as the infection cost. The problem is formulated as a mean-field game with multiclass agents which is suitable to model a massive heterogeneous IoBT system. For this game, the mean-field equilibrium is characterized, and an algorithm based on the forward backward sweep method is proposed to find the mean-field equilibrium. Then, the finite IoBT case is considered, and the conditions of convergence of the equilibria in the finite case to the mean-field equilibrium are presented. Numerical results show that the proposed scheme can achieve a 1.2-fold increase in the quality of information (QoI) compared to a baseline scheme in which the IoBT nodes are always transmitting. The results also show that the proposed scheme can reduce the proportion of infected nodes by 99% compared to the baseline.

The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas, such as smart homes, smart cities, health care, and transportation. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to battlefield specific challenges, such as the absence of communication infrastructure, heterogeneity of devices, and susceptibility to cyber-physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and information dissemination in the presence of adversaries. This paper aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to quantify the information dissemination among heterogeneous network devices. Consequently, a tractable optimization problem is formulated that can assist commanders in cost effectively planning the network and reconfiguring it according to the changing mission requirements.