To fully empower sensor networks with cognitive Internet of Things (IoT) technology, efficient medium access control protocols that enable the coexistence of cognitive sensor networks with current wireless infrastructure are as essential as the cogni
tive power in data fusion and processing due to shared wireless spectrum. Cognitive radio (CR) is introduced to increase spectrum efficiency and support such an endeavor, which thereby becomes a promising building block toward facilitating cognitive IoT. In this paper, primary users (PUs) refer to devices in existing wireless infrastructure, and secondary users (SUs) refer to cognitive sensors. For interference control between PUs and SUs, SUs adopt dynamic spectrum access and power adjustment to ensure sufficient operation of PUs, which inevitably leads to increasing latency and poses new challenges on the reliability of IoT communications. To guarantee operations of primary systems while simultaneously optimizing system performance in cognitive radio ad hoc networks (CRAHNs), this paper proposes interference-aware flooding schemes exploiting global timeout and vaccine recovery schemes to control the heavy buffer occupancy induced by packet replications. The information delivery dynamics of SUs under the proposed interference-aware recovery-assisted flooding schemes is analyzed via epidemic models and stochastic geometry from a macroscopic view of the entire system. The simulation results show that our model can efficiently capture the complicated data delivery dynamics in CRAHNs in terms of end-to-end transmission reliability and buffer occupancy. This paper sheds new light on analysis of recovery-assisted flooding schemes in CRAHNs and provides performance evaluation of cognitive IoT services built upon CRAHNs.
The various types of communication technologies and mobility features in Internet of Things (IoT) on the one hand enable fruitful and attractive applications, but on the other hand facilitates malware propagation, thereby raising new challenges on ha
ndling IoT-empowered malware for cyber security. Comparing with the malware propagation control scheme in traditional wireless networks where nodes can be directly repaired and secured, in IoT, compromised end devices are difficult to be patched. Alternatively, blocking malware via patching intermediate nodes turns out to be a more feasible and practical solution. Specifically, patching intermediate nodes can effectively prevent the proliferation of malware propagation by securing infrastructure links and limiting malware propagation to local device-to-device dissemination. This article proposes a novel traffic-aware patching scheme to select important intermediate nodes to patch, which applies to the IoT system with limited patching resources and response time constraint. Experiments on real-world trace datasets in IoT networks are conducted to demonstrate the advantage of the proposed traffic-aware patching scheme in alleviating malware propagation.
Cognitive radio technology, which is designed to enhance spectrum utilization, depends on the success of opportunistic access, where secondary users (SUs) exploit spectrum void unoccupied by primary users (PUs) for transmissions. We note that the sys
tem behaviors are very similar to the interactions among different species coexisting in an ecosystem. However, SUs of a selfish nature or of misleading information may make concurrent transmissions with PUs for additional incentives, and thus disrupt the entire ecosystem. By exploiting this vulnerability, this paper proposes a novel distributed denial-of-service (DoS) attack where invasive species, i.e., malicious users (MUs), induce originally normal-behaved SUs to execute concurrent transmissions with PUs and thus collapse the cognitive radio network. We adopt stochastic geometry to model the spatial distributions of PUs, SUs, and MUs for the analysis of the mutual interference among them. The access strategy of each SU in the spectrum sharing ecosystem, which evolves with the experienced payoffs and interference, is modeled by an evolutionary game. Based on the evolutionary stable strategy concept, we could efficiently identify the fragile operating region at which normal-behaved SUs are eventually evolved to conduct concurrent transmissions and thus to cause the ruin of the network.
The evolution of communication technology and the proliferation of electronic devices have rendered adversaries powerful means for targeted attacks via all sorts of accessible resources. In particular, owing to the intrinsic interdependency and ubiqu
itous connectivity of modern communication systems, adversaries can devise malware that propagates through intermediate hosts to approach the target, which we refer to as transmissive attacks. Inspired by biology, the transmission pattern of such an attack in the digital space much resembles the spread of an epidemic in real life. This paper elaborates transmissive attacks, summarizes the utility of epidemic models in communication systems, and draws connections between transmissive attacks and epidemic models. Simulations, experiments, and ongoing research challenges on transmissive attacks are also addressed.
To achieve end-to-end delivery in intermittently connected networks, epidemic routing is proposed for data delivery at the price of excessive buffer occupancy due to its store-and-forward nature. The ultimate goal of epidemic routing protocol design
is to reduce system resource usage (e.g., buffer occupancy) while simultaneously providing data delivery with statistical guarantee. Therefore the tradeoffs between buffer occupancy and data delivery reliability are of utmost importance. In this paper we investigate the tradeoffs for two representative schemes: the global timeout scheme and the antipacket dissemination scheme that are proposed for lossy and lossless data delivery, respectively. For lossy data delivery, we show that with the suggested global timeout value, the per-node buffer occupancy only depends on the maximum tolerable packet loss rate and pairwise meeting rate. For lossless data delivery, we show that the buffer occupancy can be significantly reduced via fully antipacket dissemination. The developed tools therefore offer new insights for epidemic routing protocol designs and performance evaluations.
Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces crowdsourcing
methods for mobile sensing. Inspired by social network, one can establish trust among participatory agents to leverage the wisdom of crowds for mobile sensing. A prototype of social network inspired mobile multimedia and sensing application is presented for illustrative purpose. Numerical experiments on real-world datasets show improved performance of mobile sensing via crowdsourcing. Challenges for mobile sensing with respect to Internet layers are discussed.
