Modeling the internet of things: a hybrid modeling approach using complex networks and agentbased models
 Komal Batool^{1} and
 Muaz A. Niazi^{2}Email author
https://doi.org/10.1186/s4029401700431
© The Author(s) 2017
Received: 28 December 2016
Accepted: 23 February 2017
Published: 24 March 2017
Abstract
Sensors, coupled with transceivers, have quickly evolved from technologies purely confined to laboratory test beds to workable solutions used across the globe. These mobile and connected devices form the nuts and bolts required to fulfill the vision of the socalled internet of things (IoT). This idea has evolved as a result of proliferation of electronic gadgets fitted with sensors and often being uniquely identifiable (possible with technological solutions such as the use of Radio Frequency Identifiers). While there is a growing need for comprehensive modeling paradigms as well as example case studies for the IoT, currently there is no standard methodology available for modeling such realworld complex IoTbased scenarios. Here, using a combination of complex networksbased and agentbased modeling approaches, we present a novel approach to modeling the IoT. Specifically, the proposed approach uses the Cognitive AgentBased Computing (CABC) framework to simulate complex IoT networks. We demonstrate modeling of several standard complex network topologies such as lattice, random, smallworld, and scalefree networks. To further demonstrate the effectiveness of the proposed approach, we also present a case study and a novel algorithm for autonomous monitoring of power consumption in networked IoT devices. We also discuss and compare the presented approach with previous approaches to modeling. Extensive simulation experiments using several network configurations demonstrate the effectiveness and viability of the proposed approach.
Keywords
Internet of things Power consumption Cognitive Agentbased Computing Agentbased modeling Complex networksIntroduction
We live in a time where electronic gadgets and integrated sensors are all around us—from versatile Smartphones and tablets to portable PCs, and from indoor temperature regulators to microwave ovens. We live in a new world—a world of smart*—where intelligence and connectivity is added to every conceivable object. The vision of the internet of things (IoT) by Ashton (2009) appears to have manifested itself—albeit in unexpected ways. This emergence of the IoT in our everyday lives obviously has numerous implications resulting in a very different environment and society.
Considering that the IoT concept is itself quite new, it is understandable that it is difficult to model. Researchers from the communication systems area often focus primarily on the communication aspects of the IoT and assume that this is essentially the whole thing. That approach however, is problematic—to say the very least. Firstly, considering only the communication aspect of the devices—with little regards to the fact that they are part of our everyday lives—is obviously incorrect. Secondly, while this would have been fine when the technologies were at the stage of proofofconcept, this is not enough at the current stage when they have even reached commercial proliferation. Additionally, a key problem in modeling the IoT using existing methodologies and tools is that the number of devices in the real world far outnumbers the one used as a proofofconcept in research papers using traditional simulation tools.
With IoT structures growing at such a fast rate, existing simulation tools such as NS2/3 and others are often unsuitable to effectively model and simulate such systems (Niazi 2008). Agentbased modeling and complex networks (Gershenson and Niazi 2013) can however be considered to model such infrastructures—such as proposed by us previously in cognitive agentbased computing framework (Niazi and Hussain 2013). This framework offers tools and techniques to effectively model different types of complex adaptive systems as well as complex physical systems such as the IoT (Niazi and Hussain 2009).
Previously, to the best of our knowledge, no work has been presented which combines different modeling paradigms from the perspective of complex systems modeling demonstrating how these paradigms may be used to effectively model complex communication networks, in general, and the IoT, in particular.
The novelty of the current paper lies in the usage of agentbased modeling in conjunction with complex networks as part of the cognitive agentbased computing framework—essentially advancing our previous work presented in Laghari and Niazi (2016). The idea is further demonstrated by means of a specific case study modeling complex network topologies for the calculation of power consumption in devices as part of the IoT.
To demonstrate the application of the cognitive agentbased computing framework, we have chosen a particular problem related to estimation of power consumption in networked sensing devices. For numerous reasons, gadgets can tend to act like “invisible” power scattering sources. Persistent power utilization additionally brings about higher airconditioning bills. The resultant impact on the planet is alarming. This is actually a phenomenon resulting from application of the basic laws of thermodynamics (Dincer 2000).
The key contributions of the current paper are summarized as follows:
We propose a methodology for modeling complex scenarios in the IoT domain using a combination of complex networks and agentbased modeling used in conjunction with each other. We would like to also note our previous introductory work in the domain published in conference proceedings such as (Batool and Niazi 2015; Batool et al. 2014). The current work, however, is both considerably more extensive as well as demonstrates the modeling of IoT dynamics in a number of realistic network topologies—namely smallworld and scalefree networks in addition to lattice, and scalefree networks.
The modeling also expands upon insights obtained from our complex network validation methodology presented earlier in (Batool and Niazi 2014).
The structure of the paper is as follows: first, necessary background to understand the methodology is described. This is followed by the model development. Furthermore, results of extensive simulation experiments are presented prior to the conclusion of the paper.
Background
In this section, a brief background is discussed. First, agentbased modeling paradigm is discussed giving details of why it is well suited for modeling and studying complex consumer networks. Next, selforganization is discussed. This is followed by a brief background of the IoT.
Agentbased modeling
The idea behind agentbased modeling (ABM) is to create models of a complex system using individuals or agents as the building blocks. This helps in not only simulating systems for design and solution of complex problems but also for the resolution of practical engineering issues (Niazi and Hussain 2011). The simulation is created to implement the operations followed by some defined rules where components interact with each other to simulate complex environments and even predict emergent behavior (Niazi 2013). This is done to develop an understanding of the complex natural environment with advanced simulation techniques (Bonabeau 2002). ABM models are commonly actualized utilizing PC reproductions either by some custom programming or particularly created ABM toolkits taking into account a more profound comprehension of the conduct of the general framework in light of the individual operators (Gershenson and Niazi 2013). To model such large complex networks agent based modeling and simulation tools are therefore a natural choice (Niazi and Hussain 2011). In previous work such as (Niazi and Hussain 2009) it has been demonstrated how complex communication networks involving autonomous and interacting agents can be modeled using these agentbased modeling tools.
Selforganization
Selforganization is the ability of a system to organize in a meaningful structure without any external force as long as certain rules are enforced in the system (Ashby 1991). This ability exits in many natural systems such as animals (Hemelrijk and Hildenbrandt 2012), human cells (Kadoshima et al. 2013), galaxies (Cen 2014) and other processes (Nicolis and Prigogine 1977) such as crystallization. Selforganization emerges as a means of maintaining the equilibrium in a system especially where the agents are interdependent.
Selforganization is considered to be an important ability in networks for successful networking (Barabási and Albert 1999). This is because the behavior is not controlled by external systems and the effect is on whole system. This organization is robust and gives the systems a capability to sustain and selfrepair the damages or problems occurring in a system. Complex network such as Smallworld and Scalefree networks also exhibit selforganizing capabilities (Wang and Chen 2003; Siemens 2005). Their organizational structure is discussed in Section V and the importance of such networks is discussed next. An alternate approach to modeling complex phenomena is using system dynamics (Azar 2012). Additionally, soft computing also offers techniques not only to model but also control complex systems (Zhu and Azar 2015).
Complex networks
One way of modeling complex adaptive systems and complex physical systems is by using complex networks (Niazi and Hussain 2013). Complex networks are loaded graphs ranging from simple networks such as lattice to random networks. These network types are important building blocks of complex systems however neither of them might behave similar to realworld empirical networks in terms of their topological structure (Newman 2010). In the real world, networks need to be analyzed to understand the exact features. Two typical network models, which have been categorized in literature in terms of corresponding to realworld networks, are the socalled smallworld and the scalefree networks.
 1.
Smallworld network
 2.
Scalefree network
 3.
Random network
 4.
Lattice network
Following is a brief introduction of these networks:
Smallworld network
 1.
Clustering coefficient: it is a measure of degree to which hubs in a chart tend to bunch together. Clustering coefficient for one node is calculated by following formula:
Clustering coefficient of entire system is ascertained by following formula:$$Ci = \frac{number\,of\,links}{Maximum\,Number\,of\,Links}$$(2)N = Number of nodes, C = Clustering coefficient for each node i.$$\begin{aligned} C = \frac{1}{N}\left(\sum\limits_{i} {Ci}\right) \hfill \\ \hfill \\ \end{aligned}$$(3)  2.
Meanshortest path length: The smallworld components has been perceived in numerous real world graphs, for example, social networks, neural systems, programming frameworks, road plans, food chains and electric power matrices and so on. Smallworld systems are made of connections of a d dimensional grid where we supplant a fraction P with connections, arbitrarily, by interjecting it among two instances of a usual lattice i.e. (P = 0) and a random graph (P = 1). Considering a ring having n vertices, among which each is associated with its k closest neighbors by undirected edges. At that point, a vertex and edge is chosen for connecting with nearest neighbor in a clockwise direction. Edge is reconnected to a vertex picked consistently at irregular over the whole ring on premise of probability p, by maintaining a strategic distance from the duplicate edges or leaving the edge set up. This procedure is rehashed on moving clockwise around the ring and considering every vertex until one lap finishes. Next, edges are viewed as the vertices connected to their secondclosest neighbors clockwise utilizing the same process as outline above. This continues until every edge in the original lattice has been considered at least once.
Scalefree network
Scalefree network is one of the complex networks in which the connectivity of nodes with the other nodes is extremely uneven. This means that there are a few nodes that have dense connectivity, while majority of the nodes have a comparatively less number of connections. Densely connected nodes are often termed as the “Hubs”. Scalefree network is considerably focused on these nodes. Modeling the dynamics in such systems plainly exhibits that a scalefree network can develop when systems increment by adding hubs to an officially existing system, and those hubs have a tendency to join specially to the hubs which are as of now very much associated. The probability that a hub will interface with k different hubs in a given system is specifically corresponding to \(k^{ \gamma}\), which suggests that a scalefree network takes after a Power Law of degree distribution of hubs.
In Eq. 4, \(k_{i}\) is the degree of hub i in real networks is with the end goal that, there is a nonzero likelihood that another hub will join to a confined hub display in the network. Heavily linked nodes called ‘Hubs are more probable and rapidly tend to accumulate much more connections, while hubs with just a couple connections are probably not going to be picked as the goal for another connection. It implies that the new hubs will have an inclination to get associated themselves to the effectively existing substantial connected hubs. The figures below, shows how this method of how the nodes choose to attachment themselves to make a scale free network. According to scalefree model, this one at a time attaching of node helps the network to grow. Then the new node in the network tend to prefer to get connected to the nodes with heavy links, meaning that it tries to attach the node near itself which has the highest degree.
Random network
Consider a network framed by interfacing diverse vertices in an arbitrary way, scientifically alluded to as the Erdős–Rényi random network model (ERDdS and Wi 1959). Formally, a random network \(G_R(n,p)\) is framed with edges associated with a likelihood, \(p\) given that 0 < \(p\) < 1.
A Random Network is shaped by associating vertices with each other in an arbitrary way. The connectivity of nodes is not dependent on the links of the nodes. For analyzing a random network, take a simple example where edges are added at random to the n isolated vertices of a graph, where n is fixed. One way to understand this is to assume that for all \(nC_{2} = \frac{n(n  1)}{2}\) edges that are viable in a graph with n vertices, there are equal chances that a particular edge will be separately added to the graph. One way is to simply add the edges at random. This is similar to tossing a coin in the air. A sub graph is created for the edge when the coin comes up heads for a particular link.
Specifically, random graphs do not provide solution to tackle with such networks, which grow with time hence additional ideas are needed to bring them to practicality, for which the complex networks have been evolved.
Lattice network
A lattice network \(G_L\) is made out of vertices with the end goal that every vertex associates with four different vertices to frame a work as a fourconnected grid. This network structure has a growing nature and thus allows unlimited capacity which increases the functionality and simplifies routing in network (Beshai 2005). The edge modules are addressed using logical coordinates, one coordinate being assigned for each of the N dimensions. This simplifies routing and permits each edge module to compute its own routing tables (Beshai 2005).
Internet of things
The IoT is a rapidly growing infrastructure and concept, primarily due to standard connectivity features in consumer electronics devices (Ashton 2009). IoT has resulted in consumer networks with remote device connectivity. Fully integrating IoT with technology will give huge opportunities for information and communication technologies (ICT) sector, which will give rise to the development of new applications, software and devices. As this area has not fully developed and needs more researches, this area is still lacking a standard architecture, which can be followed for further development and advancements. Many challenges have been recognized which hinder the deployment of IoT. Few of the challenges are the interoperability issue of devices while providing security, privacy and functionality and should have low computational power and energy capacity as it is a fundamental requirement for the upcoming solutions (Ashton 2009).
Model development for selforganized power consumption approximation algorithm
Here, we propose a model on selforganized power consumption approximation (SOPCA) algorithm in the IoT. Moreover, a brief analysis is given on the simulation environment and model development of the networks.
Definition 1 Scenario of the IoT
SOPCA algorithm
Model development of networks
Here, four different networks are modeled. Two of the networks belong to nonreal world and two are from realworld: random and lattice, smallworld and scalefree networks. Next, these network models are described.
Random network
Lattice network
Smallworld network
In order to find the clustering coefficient a function is made for it. The clusteringcoefficient and total is set initially as 0.
Next, the nodes interface with its neighbors is checked if: linkneighbors are not exactly or equivalent to one then the nodeclusteringcoefficient is set unspecified. Nodes with linkneighbors greater than 1 will play out the calculations for finding the clusteringcoefficient. Clusteringcoefficient is calculated at all nodes. It is summed and stored it altogether. Lastly, minimum separation is taken.
Scalefree network
Simulation setup
To demonstrate the utility of the proposed algorithm, it is implemented on various network topologies with different configurations discussed later. For comparison, random as well as real world networks are considered. Therefore, before modeling the complex system as more realistic networks, some fundamental principles need to be defined.
In order to test the models and SOPCA algorithm on any of the simulator, some physically meaningful and appropriate metrics to characterize both the simulated and actual event lists are identified.
Putting values in Eq. (6), where range, R = 10; \(\varepsilon_{amp} = 0.1\,{\text{nJ/bit/}}\text{m}^{2}\), \(E_{elec} = 50\, {\text{nJ/bit}}\)
Finally, total energy is obtained by adding transmit and receive energies.
Experiments and metrics

100 m × 100 m^{2} region

Number of nodes = 300, 400, 500

Number of sources = 5, 10, 15, 20

Transmission range = 10 m

Networks: random network, lattice network, small world network, scalefree network

No. of runs = 4 (1, 10, 20, 30 runs)
Results and discussion
Estimation of energy consumption
Here, we examine energy consumption estimation. The idea of energy consumption includes three separate major tasks—namely sensing, computation, and communication. Previous work has noted that communication is one of the most powerconsuming task amongst these three (Kimura and Latifi 2005). The paper estimates an almost equal transmission and reception cost for shortrange communication. Therefore, a realistic model can initialize devices with energy of 0.25 J. Besides, a transmission energy of 0.015 J may be used for messages limited to 64 bytes (Lin et al. 2012). These basic parameters are suggested to be kept for a realistic estimation of power consumption in networks.
Discussion of simulation results
Simulation parameters
Symbol  Value 

N, number of nodes  300, 400, 500 
S, number of source nodes  5 
R, transmission range  10 m 
E_{elec}, radio dissipation  50 nJ/bit 
E_{Tx}, transmitter electronics  50 nJ/bit 
E_{Rx}, receiver electronics  50 nJ/bit 
E_{amp}, transmit amplifier  0.1 nJ/bit/m^{2} 
CP_{size}, size of control packet  1 byte 
DP_{size}, size of data packet  64 bytes 
S_{energy}, initial energy of sensor nodes  0.25 J 
Power estimation using SOPCA algorithm over network, N = 500
Network  SOPCA algorithm power estimation (mJ) 

Random network  3–16 
Lattice network  4–18 
Smallworld network  2–17.5 
Scalefree network  4–22 
Networks are simulated with different configurations using ABM tool and implemented SOPCA algorithm. The algorithm was tested to analyze the power consumption effects on smaller and larger networks.
Related work
Some of the key recent papers in modeling the IoT include paper by Laghari and Niazi (2016) which apply the cognitive agentbased computing to model power consumption architecture. However, their paper does not take care of largescale complex networks such as in the domain of random/lattice, scalefree and smallworld networks.
In like manner, the paper by Altamimi and Ramadan (2016) presents a way to deal with displaying IoT using a gateway approach. Their proposed approach focuses on the use of gateways for more effective communication. Likewise Mashal et al. (2016) present the use of graphbased approaches in recommendation systems for the IoT. Zhang et al. (2016) demonstrate the use of Petri nets to model interactions between the sensors and the environment.
Conclusion
In this paper, we show for the first time, a way to deal with displaying the IoT by consolidating agentbased modeling with complex networks utilizing methods exhibited before under the cognitive agentbased computing framework. To show the proposed demonstrating system, a selforganizing distributed algorithm for dynamic approximation of power utilization in organized customer electronic gadgets is additionally exhibited. As an approval of ideas, extensive simulation experiments have been exhibited. SOPCA algorithm was tested over random, lattice, smallworld and scalefree networks. These networks are an estimate of exceptionally dense networks of consumer electronic gadgets, for example, internet of things. The newness of the exhibited work lies both in the modeling of the Internet of Things utilizing complex networks and in the utilization of agentbased models in addition to the proposed SOPCA algorithm. The essential thoughts from SOPCA algorithm can be further investigated by the assessment of flooding over these systems. The SOPCA algorithm has been implemented in realistic standard complex network topologies. Further analysis was conducted to measure energy consumption by nodes using varying metrics. In addition, we have demonstrated how the total network power consumption of SOPCA algorithm in the IoT networks can be evaluated by means of varying different metrics. In future, this work can be extended by implementing in IoT real network problem. Not only may this; to further elaborate our research work contributions, flexible simulation parameters for network strategies be used.
Declarations
Authors’ contributions
Overall, the authors contributed equally to the manuscript. Specifically, MN conceived the idea for the paper. MN and KB developed the simulation models. KB executed the simulation experiments. Both authors analyzed the simulations. Both authors wrote the paper. Both authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Funding
The authors received no specific funding for the manuscript.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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