The internet of things (IoT) has gained worldwide attention in recent years. It transforms the everyday objects that surround us into proactive actors of the Internet, generating and consuming information. An important issue related to the appearance of such large-scale self-coordinating IoT is the reliability and the collaboration between the objects in the presence of environmental hazards. High failure rates lead to significant loss of data. Therefore, data survivability is a main challenge of the IoT. In this paper, we have developed a compartmental e-Epidemic SIR (Susceptible-Infectious-Recovered) model to save the data in the network and let it survive after attacks. Furthermore, our model takes into account the dynamic topology of the network where natural death (crashing nodes) and birth are defined and analyzed. Theoretical methods and simulations are employed to solve and simulate the system of equations developed and to analyze the model.
   (1) One of the most striking features in the study of epidemics is the difficulty of finding a causal factor which appears to be adequate to account for the magnitude of the frequent epidemics of disease which visit almost every population. It was with a view to obtaining more insight regarding the effects of the various factors which govern the spread of contagious epidemics that the present investigation was undertaken. Reference may here be made to the work of Ross and Hudson (1915-17) in which the same problem is attacked. The problem is here carried to a further stage, and it is considered from a point of view which is in one sense more general. The problem may be summarised as follows: One (or more) infected person is introduced into a community of individuals, more or less susceptible to the disease in question. The disease spreads from the affected to the unaffected by contact infection. Each infected person runs through the course of his sickness, and finally is removed from the number of those who are sick, by recovery or by death. The chances of recovery or death vary from day to day during the course of his illness. The chances that the affected may convey infection to the unaffected are likewise dependent upon the stage of the sickness. As the epidemic spreads, the number of unaffected members of the community becomes reduced. Since the course of an epidemic is short compared with the life of an individual, the population may be considered as remaining constant, except in as far as it is modified by deaths due to the epidemic disease itself. In the course of time the epidemic may come to an end. One of the most important probems in epidemiology is to ascertain whether this termination occurs only when no susceptible individuals are left, or whether the interplay of the various factors of infectivity, recovery and mortality, may result in termination, whilst many susceptible individuals are still present in the unaffected population. It is difficult to treat this problem in its most general aspect. In the present communication discussion will be limited to the case in which all members of the community are initially equally susceptible to the disease, and it will be further assumed that complete immunity is conferred by a single infection. Abstract Susceptible ( S ) – exposed ( E ) – infectious ( I ) – quarantined ( Q ) – recovered ( R ) model for the transmission of malicious objects in computer network is formulated. Thresholds, equilibria, and their stability are also found with cyber mass action incidence. Threshold R cq determines the outcome of the disease. If R cq ⩽ 1, the infected fraction of the nodes disappear so the disease die out, while if R cq > 1, the infected fraction persists and the feasible region is an asymptotic stability region for the endemic equilibrium state. Numerical methods are employed to solve and simulate the system of equations developed. The effect of quarantine on recovered nodes is analyzed. We have also analyzed the behavior of the susceptible, exposed, infected, quarantine, and recovered nodes in the computer network.  The popularity of peer-to-peer (P2P) networks makes them an attractive target to the creators of viruses and other malicious code. Indeed, recently a number of viruses designed specifically to spread via P2P networks have emerged. In this paper we present a model which predicts how a P2P-based virus propagates through a network. This model is a modified version of the S-E-I (susceptible-exposed-infected) model from the field of epidemiology. Our model classifies each peer as falling into one of three categories based on the number of infected files it is sharing. We derive differential equations which comprise the deterministic model and examine the expected behaviour of the P2P network as predicted by these equations An e-epidemic SEIRS model for the transmission of worms in computer network through vertical transmission is formulated. It has been observed that if the basic reproduction number is less than or equal to one, the infected part of the nodes disappear and the worm dies out, but if the basic reproduction number is greater than one, the infected nodes exists and the worms persist at an endemic equilibrium state. Numerical methods are employed to solve and simulate the system of equations developed. We have analyzed the behavior of the susceptible, exposed, infected and recovered nodes in the computer network with real parametric values.    A wide variety of practical problems related to the interaction of agents can be examined using biological metaphors. This paper applies the theory of G-networks to agent systems by considering a biological metaphor based on three types of entities: normal cells C, cancerous or bad cells B, and immune defense agents A which are used to destroy the bad cells B, but which sometimes have the effect of being able to destroy the good cells C as well (autoimmune response). Cells of type C can mutate into cells of Type B, and vice-versa. In the presence of probabilities of correct detection and false alarm on the part of agents of Type A, we examine how the dose of agent A will influence the desired outcome which is that most bad cells B are destroyed while the damage to cells C is limited to an acceptable level. In a second part of the paper we illustrate how a similar model can be used to represent a mixture of agents with the ability to cooperate as well as to compete. 