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Due to the growing demand for spectral resources with the emergence of IoT networks and applications, cooperative cognitive radio IoT networks (CCR-IoTN) are expected to play an essential role in the world of smart technology. In this paradigm, the benefits of cooperative communication and cognitive radio networks (CRN) are merged to meet IoT networks’ needs. In this work, we employ the hybrid overlay-underlay CRN to guarantee both the secondary user (SU) stability and provide acceptable total throughput. In our CCR-IoTN, SUs may act as a relay to help the primary transmission. If the primary user (PU) is active, the secondary non-relay nodes transmit concurrently with the PU in underlay mode. In return for their cooperation, SUs are allowed to access the spectrum to transmit their data in overlay mode. In this context, we propose several fair resource allocation frameworks and compare well-known problems such as spectrum efficiency, energy efficiency as an objective function for CCR-IoTN. This kind of problem is inherently nonconvex. To solve the problems, therefore, we reformulate them as a convex problem using quadratic transform. Finally, we use simulation to evaluate the performance of the proposed hybrid resource allocation method. The resource allocation framework based on proportional fairness policy is shown to have acceptable performance in terms of throughput, fairness, and energy efficiency.

Drug delivery is one of the most important applications of molecular communication. Drug transmitters have limited resources in terms of energy and reservoir and these limitations should be taken into consideration when designing a drug delivery system. Drug molecules may also be expensive and releasing a large amount of them can have harmful effects on the healthy parts of the body. In this paper, we consider a multiple transmitter local drug delivery system in which the nearest transmitters to a randomly located tumor are activated to release drug molecules and guarantee the Least Effective Concentration (LEC) in every part of the tumor. We propose two different scenarios: a single transmitter drug delivery system for which the optimal rate of the transmitting nanomachine and the optimal density of deployed nanomachines are derived through formulations and simulations. Poisson distributed as well as regular square and hexagon grid deployments are investigated. We then extend it to a multiple transmitter drug delivery system for which the optimal allocated rate to each releasing transmitter is derived in order to minimize the total rate of release and maintain LEC in every part of the tumor. It is shown that activating multiple transmitters leads to a reduction in the total optimal release rate of drug molecules as well as improving the time duration between consecutive administrations.

In this paper, a multiple transmitter local drug delivery system associated with encapsulated drug transmitters is investigated. One of the limitations of drug delivery systems is the reservoir capacity. In order to improve the lifetime of drug transmitting nanomachines, and, hence, the longevity of drug delivery scenario, the system is associated with encapsulated drug transmitters. Encapsulated drugs are incapable of reaction with the environment unless they are unpacked in a drug transmitter nanomachine. Therefore, far-reaching transmitters do not have harmful effects on the healthy parts of the body. The advantage of this protocol is to increase the time interval between consecutive administrations without increased toxicity. As a result, it improves the mental health of patients and reduces the costs of treatment. The lifetime of this drug delivery system depends on the distribution and topology of encapsulated drug transmitters rather than their rates. Finally, a lower bound is derived on the expected lifetime of a Poisson distributed random network of nanomachines.

This paper looks into the throughput performance of a carrier sense multiple access (CSMA) protocol in Weibull fading channels. Following a power capture model, with incoherent addition of interfering signals, upper bounds and lower bounds on the throughput performance are derived analytically. Comparing the upper and lower bounds, it is seen that for propagation delays (normalized by the transmission period) less than one, the bounds are tight on the stable side of the throughput curves, i.e., for mean offered traffic rate G less than G^{*} (the mean offered traffic for which maximum throughput is achieved). In particular, the maximum point on the upper bound is seen to be very close to that on the lower bound, hence providing very tight bounds for the maximum throughput.

This paper considers a multiple transmitter approach to implement targeted drug delivery (TDD). This approach permits a distributed method of confining drug in irregular shapes of diseased tissue, as well as distributing the released rate among transmitters. A multiple transmitters TDD system is formulated as an image processing problem to derive the minimum number of transmitters, which is required to treat an arbitrary tumor of a given size.

A new approach to joint scheduling, flow control and routing in wireless networks, based on formulation as a convex optimization problem is presented. This approach is novel in that it integrates optimal scheduling and flow control with a modified version of minimum delay routing, resulting in significant performance advantage over alternative approaches. We come up with a distributed algorithm for implementation of the proposed scheme and show, by analysis and simulation, that the algorithm achieves fairness and/or priorities among users in accordance with pre-assigned user parameters. In sharp contrast to alternative algorithms that perform scheduling and packet routing based on per-session queue differential between adjacent nodes, our algorithm uses a complete multi-hop view of network conditions for packet routing and retains the desirable properties of minimum delay routing. As the result, it achieves queue sizes and end-to-end delays that are several times smaller, without compromising the throughput.

A joint routing and scheduling algorithm for multi-hop wireless networks, based on a unified convex optimization framework, is proposed. Our approach is novel in that it integrates optimal scheduling with a modified version of distributed minimum delay routing. Accordingly, the algorithm performs packet routing based on a complete multi-hop view of the network and its traffic conditions. This stands in sharp contrast to joint routing and scheduling algorithms, such as the Tassiulas algorithm, that rely on per-session queue differential between adjacent nodes for channel scheduling and packet routing. Simulation results illustrate that the proposed algorithm performs much better than Tassiulas, in terms of packet delay and jitter, packet loss and misordering, and energy consumption. Moreover, in terms of capacity region, simulation results do not reflect any noticeable difference between the two algorithms.